From 1ae20394b4027196a062a022e4beaf3f582616a9 Mon Sep 17 00:00:00 2001 From: bcjaeger <bjaeger@wakehealth.edu> Date: Thu, 5 Oct 2023 03:47:58 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20ropensci?= =?UTF-8?q?/aorsf@041805bec5bcf637e915421909b98add273a4ce2=20=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- articles/aorsf.html | 32 +-- articles/oobag.html | 18 +- .../figure-html/unnamed-chunk-2-1.png | Bin 27658 -> 27683 bytes .../figure-html/unnamed-chunk-4-1.png | Bin 45093 -> 44958 bytes .../figure-html/unnamed-chunk-7-1.png | Bin 45429 -> 49734 bytes .../figure-html/unnamed-chunk-8-1.png | Bin 48969 -> 49618 bytes articles/pd.html | 184 +++++++++--------- articles/pd_files/figure-html/-orsf_ice-1.png | Bin 586012 -> 585235 bytes .../figure-html/unnamed-chunk-13-1.png | Bin 102297 -> 101207 bytes .../figure-html/unnamed-chunk-8-1.png | Bin 42641 -> 42899 bytes .../figure-html/unnamed-chunk-9-1.png | Bin 74824 -> 74344 bytes pkgdown.yml | 2 +- reference/as.data.table.orsf_summary_uni.html | 40 ++-- reference/orsf.html | 122 +++++++----- reference/orsf_control_custom.html | 39 ++-- reference/orsf_control_net.html | 4 +- reference/orsf_ice_oob.html | 25 ++- reference/orsf_pd_oob.html | 39 ++-- reference/orsf_scale_cph.html | 2 +- reference/orsf_summarize_uni.html | 34 ++-- reference/orsf_time_to_train.html | 6 +- reference/orsf_vs.html | 50 ++--- reference/print.orsf_fit.html | 4 +- reference/print.orsf_summary_uni.html | 16 +- search.json | 2 +- 25 files changed, 320 insertions(+), 299 deletions(-) diff --git a/articles/aorsf.html b/articles/aorsf.html index 50f00f1b..e5c2edee 100644 --- a/articles/aorsf.html +++ b/articles/aorsf.html @@ -173,13 +173,13 @@ <h3 id="variable-importance">Variable importance<a class="anchor" aria-label="an <code class="sourceCode R"><span></span> <span><span class="fu"><a href="../reference/orsf_vi.html">orsf_vi_negate</a></span><span class="op">(</span><span class="va">orsf_fit</span><span class="op">)</span></span> <span><span class="co">#> bili copper protime stage sex </span></span> -<span><span class="co">#> 1.129201e-01 5.143202e-02 2.985467e-02 2.913153e-02 2.648666e-02 </span></span> +<span><span class="co">#> 1.129815e-01 5.195790e-02 2.979064e-02 2.948335e-02 2.646991e-02 </span></span> <span><span class="co">#> age albumin ascites ast chol </span></span> -<span><span class="co">#> 2.257257e-02 2.222867e-02 1.560638e-02 1.231634e-02 1.203531e-02 </span></span> +<span><span class="co">#> 2.274087e-02 2.194915e-02 1.566330e-02 1.225677e-02 1.211119e-02 </span></span> <span><span class="co">#> edema trt hepato spiders trig </span></span> -<span><span class="co">#> 9.463853e-03 7.772744e-03 6.663188e-03 6.162035e-03 5.138559e-03 </span></span> +<span><span class="co">#> 9.582936e-03 7.903782e-03 6.753772e-03 6.166109e-03 5.264650e-03 </span></span> <span><span class="co">#> alk.phos platelet </span></span> -<span><span class="co">#> 3.549245e-03 -6.782850e-06</span></span></code></pre></div> +<span><span class="co">#> 3.522158e-03 -1.435386e-05</span></span></code></pre></div> </li> <li> <p>You can also compute variable importance using permutation, a @@ -188,13 +188,13 @@ <h3 id="variable-importance">Variable importance<a class="anchor" aria-label="an <code class="sourceCode R"><span></span> <span><span class="fu"><a href="../reference/orsf_vi.html">orsf_vi_permute</a></span><span class="op">(</span><span class="va">orsf_fit</span><span class="op">)</span></span> <span><span class="co">#> bili copper protime albumin age </span></span> -<span><span class="co">#> 0.0507342790 0.0239177369 0.0163535749 0.0125096251 0.0119269625 </span></span> +<span><span class="co">#> 0.0511008531 0.0240482013 0.0163342275 0.0124345455 0.0119496253 </span></span> <span><span class="co">#> stage ascites ast edema chol </span></span> -<span><span class="co">#> 0.0115229977 0.0104655581 0.0077846459 0.0058569776 0.0048923838 </span></span> -<span><span class="co">#> spiders hepato sex trig alk.phos </span></span> -<span><span class="co">#> 0.0035124258 0.0034740982 0.0021089828 0.0019406658 0.0011960153 </span></span> +<span><span class="co">#> 0.0118205689 0.0104882375 0.0076936637 0.0060404151 0.0049699614 </span></span> +<span><span class="co">#> hepato spiders sex trig alk.phos </span></span> +<span><span class="co">#> 0.0035818563 0.0034644598 0.0021725738 0.0018539837 0.0012967692 </span></span> <span><span class="co">#> platelet trt </span></span> -<span><span class="co">#> -0.0004113343 -0.0008678193</span></span></code></pre></div> +<span><span class="co">#> -0.0003284245 -0.0009058986</span></span></code></pre></div> </li> <li> <p>A faster alternative to permutation and negation importance is @@ -204,11 +204,11 @@ <h3 id="variable-importance">Variable importance<a class="anchor" aria-label="an <code class="sourceCode R"><span></span> <span><span class="fu"><a href="../reference/orsf_vi.html">orsf_vi_anova</a></span><span class="op">(</span><span class="va">orsf_fit</span><span class="op">)</span></span> <span><span class="co">#> ascites edema bili copper albumin age protime spiders </span></span> -<span><span class="co">#> 0.4248497 0.2966406 0.2962241 0.2200782 0.2056028 0.2044682 0.1952912 0.1701295 </span></span> -<span><span class="co">#> chol ast stage hepato sex trig alk.phos platelet </span></span> -<span><span class="co">#> 0.1698671 0.1575173 0.1568678 0.1479081 0.1460362 0.1277078 0.1160302 0.1098039 </span></span> +<span><span class="co">#> 0.4244652 0.2965880 0.2955043 0.2192982 0.2059560 0.2034433 0.1946442 0.1717902 </span></span> +<span><span class="co">#> chol stage ast hepato sex trig alk.phos platelet </span></span> +<span><span class="co">#> 0.1686003 0.1575789 0.1573754 0.1476569 0.1462905 0.1272040 0.1161886 0.1089885 </span></span> <span><span class="co">#> trt </span></span> -<span><span class="co">#> 0.1094891</span></span></code></pre></div> +<span><span class="co">#> 0.1086503</span></span></code></pre></div> </li> </ul> </div> @@ -260,7 +260,7 @@ <h2 id="what-about-the-original-orsf">What about the original ORSF?<a class="anc <span> <span class="op">)</span></span> <span><span class="op">)</span></span> <span><span class="co">#> user system elapsed </span></span> -<span><span class="co">#> 5.129 0.000 5.130</span></span> +<span><span class="co">#> 6.498 0.020 6.523</span></span> <span></span> <span><span class="co"># and how long it takes to fit 50 cph trees</span></span> <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span></span> @@ -272,11 +272,11 @@ <h2 id="what-about-the-original-orsf">What about the original ORSF?<a class="anc <span> <span class="op">)</span></span> <span><span class="op">)</span></span> <span><span class="co">#> user system elapsed </span></span> -<span><span class="co">#> 0.053 0.000 0.053</span></span> +<span><span class="co">#> 0.068 0.000 0.067</span></span> <span></span> <span><span class="va">t1</span><span class="op">[</span><span class="st">'elapsed'</span><span class="op">]</span> <span class="op">/</span> <span class="va">t2</span><span class="op">[</span><span class="st">'elapsed'</span><span class="op">]</span></span> <span><span class="co">#> elapsed </span></span> -<span><span class="co">#> 96.79245</span></span></code></pre></div> +<span><span class="co">#> 97.35821</span></span></code></pre></div> </div> <div class="section level2"> <h2 id="aorsf-and-other-machine-learning-software">aorsf and other machine learning software<a class="anchor" aria-label="anchor" href="#aorsf-and-other-machine-learning-software"></a> diff --git a/articles/oobag.html b/articles/oobag.html index e8fca287..f36a8122 100644 --- a/articles/oobag.html +++ b/articles/oobag.html @@ -138,9 +138,9 @@ <h2 id="out-of-bag-predictions-and-error">Out-of-bag predictions and error<a cla <span><span class="co"># what is the output from this function?</span></span> <span><span class="va">fit</span><span class="op">$</span><span class="va">eval_oobag</span><span class="op">$</span><span class="va">stat_values</span></span> <span><span class="co">#> [,1]</span></span> -<span><span class="co">#> [1,] 0.8404084</span></span></code></pre></div> +<span><span class="co">#> [1,] 0.8405646</span></span></code></pre></div> <p>The out-of-bag estimate of Harrell’s C-statistic (the default method -to evaluate out-of-bag predictions) is 0.8404084.</p> +to evaluate out-of-bag predictions) is 0.8405646.</p> </div> <div class="section level2"> <h2 id="monitoring-out-of-bag-error">Monitoring out-of-bag error<a class="anchor" aria-label="anchor" href="#monitoring-out-of-bag-error"></a> @@ -203,7 +203,7 @@ <h2 id="user-supplied-out-of-bag-evaluation-functions">User-supplied out-of-bag <code class="sourceCode R"><span></span> <span><span class="fu">oobag_fun_brier</span><span class="op">(</span>y_mat <span class="op">=</span> <span class="va">pbc_orsf</span><span class="op">[</span>,<span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">'time'</span>, <span class="st">'status'</span><span class="op">)</span><span class="op">]</span>,</span> <span> s_vec <span class="op">=</span> <span class="va">fit</span><span class="op">$</span><span class="va">pred_oobag</span><span class="op">)</span></span> -<span><span class="co">#> [1] 0.11724</span></span></code></pre></div> +<span><span class="co">#> [1] 0.113018</span></span></code></pre></div> <p>Second, you can pass your function into <code><a href="../reference/orsf.html">orsf()</a></code>, and it will be used in place of Harrell’s C-statistic:</p> <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r"> @@ -326,12 +326,12 @@ <h2 id="user-supplied-functions-for-negation-importance-">User-supplied function <span> importance <span class="op">=</span> <span class="st">'negate'</span><span class="op">)</span></span> <span></span> <span><span class="va">fit_tdep_cstat</span><span class="op">$</span><span class="va">importance</span></span> -<span><span class="co">#> bili copper sex protime age ascites </span></span> -<span><span class="co">#> 0.130946460 0.044500890 0.033850120 0.022515610 0.019551930 0.017677020 </span></span> -<span><span class="co">#> stage albumin chol spiders edema ast </span></span> -<span><span class="co">#> 0.017561950 0.016692050 0.011163150 0.007158130 0.007008088 0.006360200 </span></span> -<span><span class="co">#> trig hepato trt alk.phos platelet </span></span> -<span><span class="co">#> 0.005541530 0.004885160 0.002620090 0.001023750 -0.002403190</span></span></code></pre></div> +<span><span class="co">#> bili copper stage sex albumin protime age </span></span> +<span><span class="co">#> 0.12277976 0.05474438 0.03624949 0.03600352 0.02799870 0.02613815 0.02258938 </span></span> +<span><span class="co">#> ascites ast chol edema spiders hepato trig </span></span> +<span><span class="co">#> 0.01396824 0.01370726 0.01291091 0.01011906 0.00679223 0.00659164 0.00615851 </span></span> +<span><span class="co">#> platelet trt alk.phos </span></span> +<span><span class="co">#> 0.00490489 0.00373202 0.00066513</span></span></code></pre></div> </div> <div class="section level2"> <h2 id="notes">Notes<a class="anchor" aria-label="anchor" href="#notes"></a> diff --git a/articles/oobag_files/figure-html/unnamed-chunk-2-1.png b/articles/oobag_files/figure-html/unnamed-chunk-2-1.png index 4c80a487ee96f57dd27cbbd7df8c6e05db8a42c2..07170287d232256e84742c91bef9a69d73090033 100644 GIT binary patch literal 27683 zcmeFZ2UOErw=Wv77i914RyNYM8bFjPy`xwNRq03<0RbTp2$7P2Wy7Z^Xo7SIN|OW# zQl$n(!O#T)Qlk=DC_x|z5JK)h@%zr%@4PYY9q*m<-W_+0lQA?IYpwrWbItmj^S|`b zHOs3)2TvRXfj~kKlS|eh&>kZYXjjYry}-!2&%g-qwfCmk)l0xP@YytOhylJ11e-X8 zf<O}Sygvd-(?7gHpg%#7OBZbKW-U%cyj8P9%C68GRxAE-=ivU`mv-+C5kXvf^y=-c 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zxzZ?hCKxE7mIfE6>VFTrzyH7dX?2Q;U*W-e4e11I`~S<Kw;}RMgxu-8iZ^gq$9??2 JWgLyU^dB=u{#^h7 diff --git a/articles/pd.html b/articles/pd.html index c573bf73..43603ef2 100644 --- a/articles/pd.html +++ b/articles/pd.html @@ -153,11 +153,11 @@ <h2 id="three-ways-to-compute-pd">Three ways to compute PD<a class="anchor" aria <span></span> <span><span class="va">pd_inb</span></span> <span><span class="co">#> pred_horizon bili mean lwr medn upr</span></span> -<span><span class="co">#> 1: 1826.25 1 0.2156600 0.02011013 0.09831473 0.8008680</span></span> -<span><span class="co">#> 2: 1826.25 2 0.2577688 0.03774419 0.15474649 0.8221929</span></span> -<span><span class="co">#> 3: 1826.25 3 0.2999502 0.06374133 0.20647008 0.8435691</span></span> -<span><span class="co">#> 4: 1826.25 4 0.3392310 0.08411776 0.25351577 0.8591273</span></span> -<span><span class="co">#> 5: 1826.25 5 0.3697834 0.10610430 0.28239158 0.8696440</span></span></code></pre></div> +<span><span class="co">#> 1: 1826.25 1 0.2154847 0.02028479 0.09620362 0.7999464</span></span> +<span><span class="co">#> 2: 1826.25 2 0.2580146 0.03766695 0.15454947 0.8215570</span></span> +<span><span class="co">#> 3: 1826.25 3 0.3001896 0.06432488 0.20728050 0.8429332</span></span> +<span><span class="co">#> 4: 1826.25 4 0.3394211 0.08427149 0.25388024 0.8601380</span></span> +<span><span class="co">#> 5: 1826.25 5 0.3703022 0.10680098 0.28301801 0.8696998</span></span></code></pre></div> </li> <li> <p>using out-of-bag predictions for the training data</p> @@ -166,12 +166,12 @@ <h2 id="three-ways-to-compute-pd">Three ways to compute PD<a class="anchor" aria <span><span class="va">pd_oob</span> <span class="op"><-</span> <span class="fu"><a href="../reference/orsf_pd_oob.html">orsf_pd_oob</a></span><span class="op">(</span><span class="va">fit</span>, pred_spec <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>bili <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fl">5</span><span class="op">)</span><span class="op">)</span></span> <span></span> <span><span class="va">pd_oob</span></span> -<span><span class="co">#> pred_horizon bili mean lwr medn upr</span></span> -<span><span class="co">#> 1: 1826.25 1 0.2151532 0.01827735 0.09723974 0.7980629</span></span> -<span><span class="co">#> 2: 1826.25 2 0.2568631 0.03685066 0.14354244 0.8181867</span></span> -<span><span class="co">#> 3: 1826.25 3 0.2986274 0.05900059 0.20896839 0.8335823</span></span> -<span><span class="co">#> 4: 1826.25 4 0.3386074 0.07902099 0.24601781 0.8482977</span></span> -<span><span class="co">#> 5: 1826.25 5 0.3696689 0.10423692 0.27977338 0.8523756</span></span></code></pre></div> +<span><span class="co">#> pred_horizon bili mean lwr medn upr</span></span> +<span><span class="co">#> 1: 1826.25 1 0.2151526 0.01835000 0.0961149 0.7980629</span></span> +<span><span class="co">#> 2: 1826.25 2 0.2572420 0.03685020 0.1444598 0.8181867</span></span> +<span><span class="co">#> 3: 1826.25 3 0.2990080 0.05900059 0.2069944 0.8335823</span></span> +<span><span class="co">#> 4: 1826.25 4 0.3388657 0.07887323 0.2434497 0.8486574</span></span> +<span><span class="co">#> 5: 1826.25 5 0.3701697 0.10614495 0.2805791 0.8523756</span></span></code></pre></div> </li> <li> <p>using predictions for a new set of data</p> @@ -183,11 +183,11 @@ <h2 id="three-ways-to-compute-pd">Three ways to compute PD<a class="anchor" aria <span></span> <span><span class="va">pd_test</span></span> <span><span class="co">#> pred_horizon bili mean lwr medn upr</span></span> -<span><span class="co">#> 1: 1826.25 1 0.2543878 0.02896385 0.1940600 0.8149734</span></span> -<span><span class="co">#> 2: 1826.25 2 0.2954799 0.05000806 0.2471401 0.8323505</span></span> -<span><span class="co">#> 3: 1826.25 3 0.3389602 0.07334663 0.3010648 0.8494444</span></span> -<span><span class="co">#> 4: 1826.25 4 0.3800023 0.10459104 0.3519063 0.8597879</span></span> -<span><span class="co">#> 5: 1826.25 5 0.4119512 0.12113773 0.3895548 0.8693355</span></span></code></pre></div> +<span><span class="co">#> 1: 1826.25 1 0.2543006 0.02901386 0.1943949 0.8140307</span></span> +<span><span class="co">#> 2: 1826.25 2 0.2956375 0.05072616 0.2473845 0.8314078</span></span> +<span><span class="co">#> 3: 1826.25 3 0.3389084 0.07453896 0.3032327 0.8485016</span></span> +<span><span class="co">#> 4: 1826.25 4 0.3800621 0.10565022 0.3516712 0.8588451</span></span> +<span><span class="co">#> 5: 1826.25 5 0.4125041 0.12292465 0.3918400 0.8694518</span></span></code></pre></div> </li> <li><p>in-bag PD indicates relationships that the model has learned during training. This is helpful if your goal is to interpret the @@ -221,8 +221,8 @@ <h2 id="one-variable-one-horizon">One variable, one horizon<a class="anchor" ari <span></span> <span><span class="va">pd_sex</span></span> <span><span class="co">#> pred_horizon sex mean lwr medn upr</span></span> -<span><span class="co">#> 1: 1826.25 m 0.3556338 0.03685843 0.2388210 0.9403551</span></span> -<span><span class="co">#> 2: 1826.25 f 0.3027138 0.01063007 0.1525368 0.9548015</span></span></code></pre></div> +<span><span class="co">#> 1: 1826.25 m 0.3564912 0.03712878 0.2369997 0.9398747</span></span> +<span><span class="co">#> 2: 1826.25 f 0.3035109 0.01053790 0.1568024 0.9545274</span></span></code></pre></div> <p>The output shows that the expected predicted mortality risk for men is substantially higher than women at 5 years after baseline.</p> </div> @@ -275,13 +275,13 @@ <h2 id="multiple-variables-marginally">Multiple variables, marginally<a class="a <span></span> <span><span class="va">pd_two_vars</span></span> <span><span class="co">#> pred_horizon variable value level mean lwr medn upr</span></span> -<span><span class="co">#> 1: 1826.25 sex NA m 0.3556338 0.03685843 0.2388210 0.9403551</span></span> -<span><span class="co">#> 2: 1826.25 sex NA f 0.3027138 0.01063007 0.1525368 0.9548015</span></span> -<span><span class="co">#> 3: 1826.25 bili 1 <NA> 0.2452129 0.01605591 0.1285905 0.8946964</span></span> -<span><span class="co">#> 4: 1826.25 bili 2 <NA> 0.3013639 0.04076854 0.2001183 0.9148909</span></span> -<span><span class="co">#> 5: 1826.25 bili 3 <NA> 0.3508351 0.05863497 0.2592947 0.9201450</span></span> -<span><span class="co">#> 6: 1826.25 bili 4 <NA> 0.3941197 0.08353114 0.3193513 0.9270573</span></span> -<span><span class="co">#> 7: 1826.25 bili 5 <NA> 0.4286023 0.10893179 0.3624517 0.9263783</span></span></code></pre></div> +<span><span class="co">#> 1: 1826.25 sex NA m 0.3564912 0.03712878 0.2369997 0.9398747</span></span> +<span><span class="co">#> 2: 1826.25 sex NA f 0.3035109 0.01053790 0.1568024 0.9545274</span></span> +<span><span class="co">#> 3: 1826.25 bili 1 <NA> 0.2461638 0.01583046 0.1295294 0.8963509</span></span> +<span><span class="co">#> 4: 1826.25 bili 2 <NA> 0.3023356 0.03962917 0.2026023 0.9165164</span></span> +<span><span class="co">#> 5: 1826.25 bili 3 <NA> 0.3519626 0.06060907 0.2635266 0.9220564</span></span> +<span><span class="co">#> 6: 1826.25 bili 4 <NA> 0.3947579 0.08420548 0.3188143 0.9267172</span></span> +<span><span class="co">#> 7: 1826.25 bili 5 <NA> 0.4293114 0.10880143 0.3618061 0.9255556</span></span></code></pre></div> <p>Now would it be tedious if you wanted to do this for all the variables? You bet. That’s why we made a function for that. As a bonus, the printed output is sorted from most to least important variables.</p> @@ -295,133 +295,133 @@ <h2 id="multiple-variables-marginally">Multiple variables, marginally<a class="a <span><span class="co">#> </span></span> <span><span class="co">#> |---------------- risk ----------------|</span></span> <span><span class="co">#> Value Mean Median 25th % 75th %</span></span> -<span><span class="co">#> 0.80 0.2376199 0.1175227 0.05112242 0.3796784</span></span> -<span><span class="co">#> 1.4 0.2639813 0.1491127 0.07001711 0.4313834</span></span> -<span><span class="co">#> 3.5 0.3750645 0.2959060 0.16393912 0.5687616</span></span> +<span><span class="co">#> 0.80 0.2384834 0.1161447 0.05083804 0.3819801</span></span> +<span><span class="co">#> 1.4 0.2650961 0.1520876 0.07038703 0.4319252</span></span> +<span><span class="co">#> 3.5 0.3757328 0.2962150 0.16422471 0.5693036</span></span> <span><span class="co">#> </span></span> <span><span class="co">#> -- copper (VI Rank: 2) -----------------------------------</span></span> <span><span class="co">#> </span></span> <span><span class="co">#> |---------------- risk ----------------|</span></span> <span><span class="co">#> Value Mean Median 25th % 75th %</span></span> -<span><span class="co">#> 43 0.2669529 0.1343897 0.05166615 0.4478705</span></span> -<span><span class="co">#> 74 0.2910106 0.1560732 0.06745016 0.4930575</span></span> -<span><span class="co">#> 129 0.3458559 0.2276424 0.10882113 0.5518172</span></span> +<span><span class="co">#> 43 0.2674680 0.1379648 0.05160351 0.4454352</span></span> +<span><span class="co">#> 74 0.2916843 0.1599240 0.06762896 0.4988722</span></span> +<span><span class="co">#> 129 0.3468187 0.2274629 0.11003062 0.5575685</span></span> <span><span class="co">#> </span></span> <span><span class="co">#> -- sex (VI Rank: 3) --------------------------------------</span></span> <span><span class="co">#> </span></span> <span><span class="co">#> |---------------- risk ----------------|</span></span> <span><span class="co">#> Value Mean Median 25th % 75th %</span></span> -<span><span class="co">#> m 0.3556338 0.2388210 0.10652523 0.5860674</span></span> -<span><span class="co">#> f 0.3027138 0.1525368 0.05384831 0.5617088</span></span> +<span><span class="co">#> m 0.3564912 0.2369997 0.10787696 0.5872914</span></span> +<span><span class="co">#> f 0.3035109 0.1568024 0.05434944 0.5526196</span></span> <span><span class="co">#> </span></span> <span><span class="co">#> -- stage (VI Rank: 4) ------------------------------------</span></span> <span><span class="co">#> </span></span> <span><span class="co">#> |---------------- risk ----------------|</span></span> -<span><span class="co">#> Value Mean Median 25th % 75th %</span></span> -<span><span class="co">#> 1 0.5826297 0.5285612 0.3996661 0.7538155</span></span> -<span><span class="co">#> 2 0.5826297 0.5285612 0.3996661 0.7538155</span></span> -<span><span class="co">#> 3 0.5826297 0.5285612 0.3996661 0.7538155</span></span> -<span><span class="co">#> 4 0.5826297 0.5285612 0.3996661 0.7538155</span></span> +<span><span class="co">#> Value Mean Median 25th % 75th %</span></span> +<span><span class="co">#> 1 0.5847147 0.52941 0.3996593 0.7568616</span></span> +<span><span class="co">#> 2 0.5847147 0.52941 0.3996593 0.7568616</span></span> +<span><span class="co">#> 3 0.5847147 0.52941 0.3996593 0.7568616</span></span> +<span><span class="co">#> 4 0.5847147 0.52941 0.3996593 0.7568616</span></span> <span><span class="co">#> </span></span> <span><span class="co">#> -- age (VI Rank: 5) --------------------------------------</span></span> <span><span class="co">#> </span></span> <span><span class="co">#> |---------------- risk ----------------|</span></span> <span><span class="co">#> Value Mean Median 25th % 75th %</span></span> -<span><span class="co">#> 42 0.2759266 0.1460143 0.04501236 0.4636428</span></span> -<span><span class="co">#> 50 0.3096357 0.1961013 0.05299745 0.5246981</span></span> -<span><span class="co">#> 57 0.3454927 0.2378101 0.08210052 0.5872838</span></span> +<span><span class="co">#> 42 0.2769998 0.1475089 0.04641807 0.4656813</span></span> +<span><span class="co">#> 50 0.3105800 0.1962849 0.05238234 0.5260957</span></span> +<span><span class="co">#> 57 0.3458171 0.2416166 0.08073215 0.5838783</span></span> <span><span class="co">#> </span></span> <span><span class="co">#> -- protime (VI Rank: 6) ----------------------------------</span></span> <span><span class="co">#> </span></span> <span><span class="co">#> |---------------- risk ----------------|</span></span> <span><span class="co">#> Value Mean Median 25th % 75th %</span></span> -<span><span class="co">#> 10 0.2863813 0.1552936 0.05388682 0.5048504</span></span> -<span><span class="co">#> 11 0.3040031 0.1631759 0.05897970 0.5387056</span></span> -<span><span class="co">#> 11 0.3268747 0.1901532 0.06986700 0.5837946</span></span> +<span><span class="co">#> 10 0.2868796 0.1565407 0.05348739 0.5082739</span></span> +<span><span class="co">#> 11 0.3049211 0.1630376 0.05660416 0.5398259</span></span> +<span><span class="co">#> 11 0.3279135 0.1941268 0.07126535 0.5817064</span></span> <span><span class="co">#> </span></span> <span><span class="co">#> -- albumin (VI Rank: 7) ----------------------------------</span></span> <span><span class="co">#> </span></span> <span><span class="co">#> |---------------- risk ----------------|</span></span> <span><span class="co">#> Value Mean Median 25th % 75th %</span></span> -<span><span class="co">#> 3.3 0.3262871 0.1903782 0.06054024 0.6108546</span></span> -<span><span class="co">#> 3.5 0.3036352 0.1556788 0.05654525 0.5458010</span></span> -<span><span class="co">#> 3.8 0.2855665 0.1540199 0.05336999 0.5033495</span></span> +<span><span class="co">#> 3.3 0.3268318 0.1898688 0.06087156 0.6078501</span></span> +<span><span class="co">#> 3.5 0.3040046 0.1564467 0.05753060 0.5400452</span></span> +<span><span class="co">#> 3.8 0.2861643 0.1576060 0.05303617 0.5026478</span></span> <span><span class="co">#> </span></span> <span><span class="co">#> -- ascites (VI Rank: 8) ----------------------------------</span></span> <span><span class="co">#> </span></span> <span><span class="co">#> |---------------- risk ----------------|</span></span> <span><span class="co">#> Value Mean Median 25th % 75th %</span></span> -<span><span class="co">#> 0 0.3020660 0.1499472 0.05384831 0.5426342</span></span> -<span><span class="co">#> 1 0.4518423 0.3638425 0.24341274 0.6433297</span></span> +<span><span class="co">#> 0 0.3028570 0.1542828 0.05434944 0.5393245</span></span> +<span><span class="co">#> 1 0.4525678 0.3667405 0.24229217 0.6413067</span></span> <span><span class="co">#> </span></span> -<span><span class="co">#> -- ast (VI Rank: 9) --------------------------------------</span></span> +<span><span class="co">#> -- chol (VI Rank: 9) -------------------------------------</span></span> <span><span class="co">#> </span></span> <span><span class="co">#> |---------------- risk ----------------|</span></span> <span><span class="co">#> Value Mean Median 25th % 75th %</span></span> -<span><span class="co">#> 82 0.2892887 0.1466898 0.05112062 0.5135115</span></span> -<span><span class="co">#> 117 0.3067945 0.1587376 0.05443293 0.5522869</span></span> -<span><span class="co">#> 153 0.3298465 0.1791157 0.07169660 0.6069204</span></span> +<span><span class="co">#> 250 0.2931503 0.1529514 0.04780508 0.5046722</span></span> +<span><span class="co">#> 310 0.3029789 0.1662991 0.05563008 0.5144736</span></span> +<span><span class="co">#> 401 0.3256828 0.1983822 0.07294784 0.5492762</span></span> <span><span class="co">#> </span></span> -<span><span class="co">#> -- chol (VI Rank: 10) ------------------------------------</span></span> +<span><span class="co">#> -- ast (VI Rank: 10) -------------------------------------</span></span> <span><span class="co">#> </span></span> <span><span class="co">#> |---------------- risk ----------------|</span></span> <span><span class="co">#> Value Mean Median 25th % 75th %</span></span> -<span><span class="co">#> 250 0.2927177 0.1496405 0.04763950 0.5048974</span></span> -<span><span class="co">#> 310 0.3023735 0.1576780 0.05465480 0.5158055</span></span> -<span><span class="co">#> 401 0.3248603 0.1923752 0.07233946 0.5526811</span></span> +<span><span class="co">#> 82 0.2903896 0.1490766 0.05113070 0.5159897</span></span> +<span><span class="co">#> 117 0.3076024 0.1648002 0.05698760 0.5501132</span></span> +<span><span class="co">#> 153 0.3307164 0.1800020 0.07086705 0.6059466</span></span> <span><span class="co">#> </span></span> <span><span class="co">#> -- edema (VI Rank: 11) -----------------------------------</span></span> <span><span class="co">#> </span></span> <span><span class="co">#> |---------------- risk ----------------|</span></span> -<span><span class="co">#> Value Mean Median 25th % 75th %</span></span> -<span><span class="co">#> 0 0.2959085 0.1499472 0.05285512 0.5406550</span></span> -<span><span class="co">#> 0.5 0.3637250 0.2550075 0.10860243 0.6126169</span></span> -<span><span class="co">#> 1 0.4575163 0.3709865 0.25421885 0.6489456</span></span> +<span><span class="co">#> Value Mean Median 25th % 75th %</span></span> +<span><span class="co">#> 0 0.2968877 0.1534589 0.0539005 0.5366297</span></span> +<span><span class="co">#> 0.5 0.3639845 0.2581248 0.1059203 0.6140513</span></span> +<span><span class="co">#> 1 0.4571985 0.3727463 0.2549830 0.6499621</span></span> <span><span class="co">#> </span></span> <span><span class="co">#> -- spiders (VI Rank: 12) ---------------------------------</span></span> <span><span class="co">#> </span></span> <span><span class="co">#> |---------------- risk ----------------|</span></span> <span><span class="co">#> Value Mean Median 25th % 75th %</span></span> -<span><span class="co">#> 0 0.2976539 0.1499472 0.05137698 0.5310312</span></span> -<span><span class="co">#> 1 0.3426720 0.2230450 0.09443604 0.5593377</span></span> +<span><span class="co">#> 0 0.2984039 0.1528105 0.05324006 0.5321606</span></span> +<span><span class="co">#> 1 0.3438490 0.2238338 0.09693236 0.5660642</span></span> <span><span class="co">#> </span></span> <span><span class="co">#> -- hepato (VI Rank: 13) ----------------------------------</span></span> <span><span class="co">#> </span></span> <span><span class="co">#> |---------------- risk ----------------|</span></span> <span><span class="co">#> Value Mean Median 25th % 75th %</span></span> -<span><span class="co">#> 0 0.2912547 0.1493194 0.05056845 0.5242976</span></span> -<span><span class="co">#> 1 0.3268190 0.1830504 0.07537275 0.5490061</span></span> +<span><span class="co">#> 0 0.2924451 0.1513041 0.05269621 0.5291405</span></span> +<span><span class="co">#> 1 0.3274109 0.1826162 0.07423325 0.5488156</span></span> <span><span class="co">#> </span></span> <span><span class="co">#> -- trt (VI Rank: 14) -------------------------------------</span></span> <span><span class="co">#> </span></span> <span><span class="co">#> |---------------- risk ----------------|</span></span> <span><span class="co">#> Value Mean Median 25th % 75th %</span></span> -<span><span class="co">#> d_penicill_main 0.3139442 0.1728924 0.06002565 0.5541061</span></span> -<span><span class="co">#> placebo 0.3083033 0.1575168 0.05420006 0.5508226</span></span> +<span><span class="co">#> d_penicill_main 0.3146055 0.1808374 0.06225388 0.5581896</span></span> +<span><span class="co">#> placebo 0.3092247 0.1616993 0.05455801 0.5462009</span></span> <span><span class="co">#> </span></span> <span><span class="co">#> -- trig (VI Rank: 15) ------------------------------------</span></span> <span><span class="co">#> </span></span> <span><span class="co">#> |---------------- risk ----------------|</span></span> <span><span class="co">#> Value Mean Median 25th % 75th %</span></span> -<span><span class="co">#> 85 0.3011420 0.1531589 0.05085489 0.5339691</span></span> -<span><span class="co">#> 108 0.3087066 0.1626084 0.05129435 0.5384522</span></span> -<span><span class="co">#> 151 0.3229588 0.1833097 0.06437458 0.5452205</span></span> +<span><span class="co">#> 85 0.3020625 0.1594529 0.04947666 0.5368518</span></span> +<span><span class="co">#> 108 0.3094585 0.1642559 0.05138019 0.5381577</span></span> +<span><span class="co">#> 151 0.3236035 0.1919256 0.06579255 0.5484254</span></span> <span><span class="co">#> </span></span> <span><span class="co">#> -- alk.phos (VI Rank: 16) --------------------------------</span></span> <span><span class="co">#> </span></span> <span><span class="co">#> |---------------- risk ----------------|</span></span> <span><span class="co">#> Value Mean Median 25th % 75th %</span></span> -<span><span class="co">#> 922 0.3123888 0.1719807 0.05639797 0.5655804</span></span> -<span><span class="co">#> 1278 0.3140034 0.1688451 0.05799607 0.5687561</span></span> -<span><span class="co">#> 2068 0.3170148 0.1684038 0.05899212 0.5944319</span></span> +<span><span class="co">#> 922 0.3125063 0.1746096 0.05547749 0.5655240</span></span> +<span><span class="co">#> 1278 0.3143534 0.1730928 0.06012625 0.5716067</span></span> +<span><span class="co">#> 2068 0.3175794 0.1719252 0.05909212 0.5968980</span></span> <span><span class="co">#> </span></span> <span><span class="co">#> -- platelet (VI Rank: 17) --------------------------------</span></span> <span><span class="co">#> </span></span> <span><span class="co">#> |---------------- risk ----------------|</span></span> <span><span class="co">#> Value Mean Median 25th % 75th %</span></span> -<span><span class="co">#> 200 0.3169215 0.1771280 0.05563475 0.5902139</span></span> -<span><span class="co">#> 257 0.3112003 0.1692829 0.05472123 0.5747745</span></span> -<span><span class="co">#> 318 0.3078816 0.1724278 0.05521177 0.5625068</span></span> +<span><span class="co">#> 200 0.3178789 0.1797536 0.05520746 0.5855790</span></span> +<span><span class="co">#> 257 0.3120217 0.1700296 0.05532263 0.5736814</span></span> +<span><span class="co">#> 318 0.3087421 0.1739535 0.05590358 0.5649877</span></span> <span><span class="co">#> </span></span> <span><span class="co">#> Predicted risk at time t = 1826.25 for top 17 predictors</span></span></code></pre></div> <p>It’s easy enough to turn this ‘summary’ object into a @@ -430,12 +430,12 @@ <h2 id="multiple-variables-marginally">Multiple variables, marginally<a class="a <code class="sourceCode R"><span></span> <span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="fu"><a href="https://Rdatatable.gitlab.io/data.table/reference/as.data.table.html" class="external-link">as.data.table</a></span><span class="op">(</span><span class="va">pd_smry</span><span class="op">)</span><span class="op">)</span></span> <span><span class="co">#> variable importance Value Mean Median 25th % 75th %</span></span> -<span><span class="co">#> 1: bili 0.11347395 0.80 0.2376199 0.1175227 0.05112242 0.3796784</span></span> -<span><span class="co">#> 2: bili 0.11347395 1.4 0.2639813 0.1491127 0.07001711 0.4313834</span></span> -<span><span class="co">#> 3: bili 0.11347395 3.5 0.3750645 0.2959060 0.16393912 0.5687616</span></span> -<span><span class="co">#> 4: copper 0.04899814 43 0.2669529 0.1343897 0.05166615 0.4478705</span></span> -<span><span class="co">#> 5: copper 0.04899814 74 0.2910106 0.1560732 0.06745016 0.4930575</span></span> -<span><span class="co">#> 6: copper 0.04899814 129 0.3458559 0.2276424 0.10882113 0.5518172</span></span> +<span><span class="co">#> 1: bili 0.11319592 0.80 0.2384834 0.1161447 0.05083804 0.3819801</span></span> +<span><span class="co">#> 2: bili 0.11319592 1.4 0.2650961 0.1520876 0.07038703 0.4319252</span></span> +<span><span class="co">#> 3: bili 0.11319592 3.5 0.3757328 0.2962150 0.16422471 0.5693036</span></span> +<span><span class="co">#> 4: copper 0.04949475 43 0.2674680 0.1379648 0.05160351 0.4454352</span></span> +<span><span class="co">#> 5: copper 0.04949475 74 0.2916843 0.1599240 0.06762896 0.4988722</span></span> +<span><span class="co">#> 6: copper 0.04949475 129 0.3468187 0.2274629 0.11003062 0.5575685</span></span> <span><span class="co">#> pred_horizon level</span></span> <span><span class="co">#> 1: 1826.25 <NA></span></span> <span><span class="co">#> 2: 1826.25 <NA></span></span> @@ -531,17 +531,17 @@ <h2 id="visualizing-ice-curves">Visualizing ICE curves<a class="anchor" aria-lab <span></span> <span><span class="va">ice_oob</span></span> <span><span class="co">#> id_variable id_row pred_horizon bili pred</span></span> -<span><span class="co">#> 1: 1 1 1826.25 1 0.9216359</span></span> -<span><span class="co">#> 2: 1 2 1826.25 1 0.1141265</span></span> -<span><span class="co">#> 3: 1 3 1826.25 1 0.7337662</span></span> -<span><span class="co">#> 4: 1 4 1826.25 1 0.3585570</span></span> -<span><span class="co">#> 5: 1 5 1826.25 1 0.1417314</span></span> +<span><span class="co">#> 1: 1 1 1826.25 1 0.9194969</span></span> +<span><span class="co">#> 2: 1 2 1826.25 1 0.1136944</span></span> +<span><span class="co">#> 3: 1 3 1826.25 1 0.7413338</span></span> +<span><span class="co">#> 4: 1 4 1826.25 1 0.3671091</span></span> +<span><span class="co">#> 5: 1 5 1826.25 1 0.1439086</span></span> <span><span class="co">#> --- </span></span> -<span><span class="co">#> 6896: 25 272 1826.25 10 0.3264152</span></span> -<span><span class="co">#> 6897: 25 273 1826.25 10 0.4338510</span></span> -<span><span class="co">#> 6898: 25 274 1826.25 10 0.4856256</span></span> -<span><span class="co">#> 6899: 25 275 1826.25 10 0.3136700</span></span> -<span><span class="co">#> 6900: 25 276 1826.25 10 0.5347941</span></span></code></pre></div> +<span><span class="co">#> 6896: 25 272 1826.25 10 0.3218517</span></span> +<span><span class="co">#> 6897: 25 273 1826.25 10 0.4362345</span></span> +<span><span class="co">#> 6898: 25 274 1826.25 10 0.4962449</span></span> +<span><span class="co">#> 6899: 25 275 1826.25 10 0.3131265</span></span> +<span><span class="co">#> 6900: 25 276 1826.25 10 0.5433389</span></span></code></pre></div> <ul> <li><p><code>id_variable</code> is an identifier for the current value of the variable(s) that are in the data. 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c7395436..7f116c56 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -5,7 +5,7 @@ articles: aorsf: aorsf.html oobag: oobag.html pd: pd.html -last_built: 2023-10-04T14:00Z +last_built: 2023-10-05T03:45Z urls: reference: https://bcjaeger.github.io/aorsf/reference article: https://bcjaeger.github.io/aorsf/articles diff --git a/reference/as.data.table.orsf_summary_uni.html b/reference/as.data.table.orsf_summary_uni.html index 84995cfd..73155432 100644 --- a/reference/as.data.table.orsf_summary_uni.html +++ b/reference/as.data.table.orsf_summary_uni.html @@ -94,28 +94,24 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e <span class="r-in"><span><span class="va">smry</span> <span class="op"><-</span> <span class="fu"><a href="orsf_summarize_uni.html">orsf_summarize_uni</a></span><span class="op">(</span><span class="va">object</span>, n_variables <span class="op">=</span> <span class="fl">3</span><span class="op">)</span></span></span> <span class="r-in"><span></span></span> <span class="r-in"><span><span class="fu"><a href="https://Rdatatable.gitlab.io/data.table/reference/as.data.table.html" class="external-link">as.data.table</a></span><span class="op">(</span><span class="va">smry</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> variable importance value mean medn lwr upr</span> -<span class="r-out co"><span class="r-pr">#></span> 1: bili 0.12308231 0.80 0.2367744 0.1212470 0.05279761 0.3629509</span> -<span class="r-out co"><span class="r-pr">#></span> 2: bili 0.12308231 1.4 0.2637149 0.1453019 0.07504962 0.4062557</span> -<span class="r-out co"><span class="r-pr">#></span> 3: bili 0.12308231 3.5 0.3806357 0.2922601 0.17581820 0.5671431</span> -<span class="r-out co"><span class="r-pr">#></span> 4: copper 0.04831185 43 0.2727479 0.1435153 0.05340342 0.4887602</span> -<span class="r-out co"><span class="r-pr">#></span> 5: copper 0.04831185 74 0.2962180 0.1684203 0.06767541 0.5202639</span> -<span class="r-out co"><span class="r-pr">#></span> 6: copper 0.04831185 129 0.3481921 0.2290269 0.11586820 0.5679235</span> -<span class="r-out co"><span class="r-pr">#></span> 7: stage 0.03206122 1 0.5778359 0.5196442 0.39788860 0.7723695</span> -<span class="r-out co"><span class="r-pr">#></span> 8: stage 0.03206122 2 0.5778359 0.5196442 0.39788860 0.7723695</span> -<span class="r-out co"><span class="r-pr">#></span> 9: stage 0.03206122 3 0.5778359 0.5196442 0.39788860 0.7723695</span> -<span class="r-out co"><span class="r-pr">#></span> 10: stage 0.03206122 4 0.5778359 0.5196442 0.39788860 0.7723695</span> -<span class="r-out co"><span class="r-pr">#></span> pred_horizon level</span> -<span class="r-out co"><span class="r-pr">#></span> 1: 1788 <NA></span> -<span class="r-out co"><span class="r-pr">#></span> 2: 1788 <NA></span> -<span class="r-out co"><span class="r-pr">#></span> 3: 1788 <NA></span> -<span class="r-out co"><span class="r-pr">#></span> 4: 1788 <NA></span> -<span class="r-out co"><span class="r-pr">#></span> 5: 1788 <NA></span> -<span class="r-out co"><span class="r-pr">#></span> 6: 1788 <NA></span> -<span class="r-out co"><span class="r-pr">#></span> 7: 1788 1</span> -<span class="r-out co"><span class="r-pr">#></span> 8: 1788 2</span> -<span class="r-out co"><span class="r-pr">#></span> 9: 1788 3</span> -<span class="r-out co"><span class="r-pr">#></span> 10: 1788 4</span> +<span class="r-out co"><span class="r-pr">#></span> variable importance value mean medn lwr upr</span> +<span class="r-out co"><span class="r-pr">#></span> 1: bili 0.11307855 0.80 0.2371490 0.1202006 0.05116040 0.3821702</span> +<span class="r-out co"><span class="r-pr">#></span> 2: bili 0.11307855 1.4 0.2651162 0.1487302 0.06791377 0.4193537</span> +<span class="r-out co"><span class="r-pr">#></span> 3: bili 0.11307855 3.5 0.3720891 0.2833817 0.15276030 0.5680952</span> +<span class="r-out co"><span class="r-pr">#></span> 4: copper 0.05610062 43 0.2651930 0.1403332 0.05421519 0.4502802</span> +<span class="r-out co"><span class="r-pr">#></span> 5: copper 0.05610062 74 0.2926748 0.1647021 0.07409095 0.5005956</span> +<span class="r-out co"><span class="r-pr">#></span> 6: copper 0.05610062 129 0.3539127 0.2412332 0.12181162 0.5944691</span> +<span class="r-out co"><span class="r-pr">#></span> 7: sex 0.03438589 m 0.3541657 0.2294593 0.11517133 0.5905949</span> +<span class="r-out co"><span class="r-pr">#></span> 8: sex 0.03438589 f 0.3049946 0.1560052 0.05105105 0.5552089</span> +<span class="r-out co"><span class="r-pr">#></span> pred_horizon level</span> +<span class="r-out co"><span class="r-pr">#></span> 1: 1788 <NA></span> +<span class="r-out co"><span class="r-pr">#></span> 2: 1788 <NA></span> +<span class="r-out co"><span class="r-pr">#></span> 3: 1788 <NA></span> +<span class="r-out co"><span class="r-pr">#></span> 4: 1788 <NA></span> +<span class="r-out co"><span class="r-pr">#></span> 5: 1788 <NA></span> +<span class="r-out co"><span class="r-pr">#></span> 6: 1788 <NA></span> +<span class="r-out co"><span class="r-pr">#></span> 7: 1788 m</span> +<span class="r-out co"><span class="r-pr">#></span> 8: 1788 f</span> <span class="r-in"><span></span></span> </code></pre></div> </div> diff --git a/reference/orsf.html b/reference/orsf.html index 5e7aee62..cb523951 100644 --- a/reference/orsf.html +++ b/reference/orsf.html @@ -523,6 +523,32 @@ <h4 id="linear-combinations-with-your-own-function">Linear combinations with you <span> <span class="va">pca</span><span class="op">$</span><span class="va">rotation</span><span class="op">[</span>, <span class="fl">1L</span>, drop <span class="op">=</span> <span class="cn">FALSE</span><span class="op">]</span></span> <span></span> <span><span class="op">}</span></span></code></pre><p></p></div></li> +<li><p>The third uses <code>orsf()</code> inside of <code>orsf()</code> (aka reinforcement learning +trees RLTs).</p> +<p></p><div class="sourceCode r"><pre><code><span><span class="co"># some special care is taken to prevent your R session from crashing.</span></span> +<span><span class="co"># Specifically, random coefficients are used when n_obs <= 10</span></span> +<span><span class="co"># or n_events <= 5. </span></span> +<span></span> +<span><span class="va">f_aorsf</span> <span class="op"><-</span> <span class="kw">function</span><span class="op">(</span><span class="va">x_node</span>, <span class="va">y_node</span>, <span class="va">w_node</span><span class="op">)</span><span class="op">{</span></span> +<span></span> +<span> <span class="fu"><a href="https://rdrr.io/r/base/colnames.html" class="external-link">colnames</a></span><span class="op">(</span><span class="va">y_node</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">'time'</span>, <span class="st">'status'</span><span class="op">)</span></span> +<span> <span class="fu"><a href="https://rdrr.io/r/base/colnames.html" class="external-link">colnames</a></span><span class="op">(</span><span class="va">x_node</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"x"</span>, <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">ncol</a></span><span class="op">(</span><span class="va">x_node</span><span class="op">)</span><span class="op">)</span>, sep <span class="op">=</span> <span class="st">''</span><span class="op">)</span></span> +<span></span> +<span> <span class="va">data</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/as.data.frame.html" class="external-link">as.data.frame</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">cbind</a></span><span class="op">(</span><span class="va">y_node</span>, <span class="va">x_node</span><span class="op">)</span><span class="op">)</span></span> +<span></span> +<span> <span class="kw">if</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">data</span><span class="op">)</span> <span class="op"><=</span> <span class="fl">10</span> <span class="op">||</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">y_node</span><span class="op">[</span>,<span class="st">'status'</span><span class="op">]</span><span class="op">)</span> <span class="op"><=</span> <span class="fl">5</span><span class="op">)</span> </span> +<span> <span class="kw"><a href="https://rdrr.io/r/base/function.html" class="external-link">return</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Uniform.html" class="external-link">runif</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">ncol</a></span><span class="op">(</span><span class="va">x_node</span><span class="op">)</span><span class="op">)</span>, ncol <span class="op">=</span> <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span> +<span></span> +<span> <span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="../reference/orsf.html">orsf</a></span><span class="op">(</span><span class="va">data</span>, <span class="va">time</span> <span class="op">+</span> <span class="va">status</span> <span class="op">~</span> <span class="va">.</span>, </span> +<span> weights <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">as.numeric</a></span><span class="op">(</span><span class="va">w_node</span><span class="op">)</span>,</span> +<span> n_tree <span class="op">=</span> <span class="fl">25</span>,</span> +<span> importance <span class="op">=</span> <span class="st">'anova'</span><span class="op">)</span></span> +<span></span> +<span> <span class="va">out</span> <span class="op"><-</span> <span class="fu"><a href="../reference/orsf_vi.html">orsf_vi</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/colnames.html" class="external-link">colnames</a></span><span class="op">(</span><span class="va">x_node</span><span class="op">)</span><span class="op">]</span></span> +<span></span> +<span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="va">out</span>, ncol <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span> +<span></span> +<span><span class="op">}</span></span></code></pre><p></p></div></li> </ul><p>We can plug these functions into <code><a href="orsf_control_custom.html">orsf_control_custom()</a></code>, and then pass the result into <code>orsf()</code>:</p> <p></p><div class="sourceCode r"><pre><code><span><span class="va">fit_rando</span> <span class="op"><-</span> <span class="fu"><a href="../reference/orsf.html">orsf</a></span><span class="op">(</span><span class="va">pbc_orsf</span>,</span> @@ -533,6 +559,10 @@ <h4 id="linear-combinations-with-your-own-function">Linear combinations with you <span><span class="va">fit_pca</span> <span class="op"><-</span> <span class="fu"><a href="../reference/orsf.html">orsf</a></span><span class="op">(</span><span class="va">pbc_orsf</span>,</span> <span> <span class="fu"><a href="https://rdrr.io/pkg/survival/man/Surv.html" class="external-link">Surv</a></span><span class="op">(</span><span class="va">time</span>, <span class="va">status</span><span class="op">)</span> <span class="op">~</span> <span class="va">.</span> <span class="op">-</span> <span class="va">id</span>,</span> <span> control <span class="op">=</span> <span class="fu"><a href="../reference/orsf_control_custom.html">orsf_control_custom</a></span><span class="op">(</span>beta_fun <span class="op">=</span> <span class="va">f_pca</span><span class="op">)</span>,</span> +<span> tree_seeds <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fl">500</span><span class="op">)</span></span> +<span></span> +<span><span class="va">fit_rlt</span> <span class="op"><-</span> <span class="fu"><a href="../reference/orsf.html">orsf</a></span><span class="op">(</span><span class="va">pbc_orsf</span>, <span class="va">time</span> <span class="op">+</span> <span class="va">status</span> <span class="op">~</span> <span class="va">.</span> <span class="op">-</span> <span class="va">id</span>, </span> +<span> control <span class="op">=</span> <span class="fu"><a href="../reference/orsf_control_custom.html">orsf_control_custom</a></span><span class="op">(</span>beta_fun <span class="op">=</span> <span class="va">f_aorsf</span><span class="op">)</span>,</span> <span> tree_seeds <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fl">500</span><span class="op">)</span></span></code></pre><p></p></div> <p>So which fit seems to work best in this example? Let’s find out by evaluating the out-of-bag survival predictions.</p> @@ -541,7 +571,8 @@ <h4 id="linear-combinations-with-your-own-function">Linear combinations with you <span> cph <span class="op">=</span> <span class="fl">1</span> <span class="op">-</span> <span class="va">fit_cph</span><span class="op">$</span><span class="va">pred_oobag</span>,</span> <span> net <span class="op">=</span> <span class="fl">1</span> <span class="op">-</span> <span class="va">fit_net</span><span class="op">$</span><span class="va">pred_oobag</span>,</span> <span> rando <span class="op">=</span> <span class="fl">1</span> <span class="op">-</span> <span class="va">fit_rando</span><span class="op">$</span><span class="va">pred_oobag</span>,</span> -<span> pca <span class="op">=</span> <span class="fl">1</span> <span class="op">-</span> <span class="va">fit_pca</span><span class="op">$</span><span class="va">pred_oobag</span></span> +<span> pca <span class="op">=</span> <span class="fl">1</span> <span class="op">-</span> <span class="va">fit_pca</span><span class="op">$</span><span class="va">pred_oobag</span>,</span> +<span> rlt <span class="op">=</span> <span class="fl">1</span> <span class="op">-</span> <span class="va">fit_rlt</span><span class="op">$</span><span class="va">pred_oobag</span></span> <span><span class="op">)</span></span> <span></span> <span><span class="va">sc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/riskRegression/man/Score.html" class="external-link">Score</a></span><span class="op">(</span>object <span class="op">=</span> <span class="va">risk_preds</span>, </span> @@ -555,8 +586,9 @@ <h4 id="linear-combinations-with-your-own-function">Linear combinations with you <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="do">## 1: net 1788 0.9179396 0.02012887 0.8784877 0.9573915</span></span> <span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="do">## 2: accel 1788 0.9106396 0.02076004 0.8699507 0.9513286</span></span> <span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="do">## 3: cph 1788 0.9061167 0.02277540 0.8614777 0.9507556</span></span> -<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="do">## 4: rando 1788 0.8997729 0.02201363 0.8566270 0.9429188</span></span> -<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="do">## 5: pca 1788 0.8996927 0.02245483 0.8556821 0.9437034</span></span></code></pre><p></p></div> +<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="do">## 4: rlt 1788 0.9012605 0.02178982 0.8585533 0.9439678</span></span> +<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="do">## 5: rando 1788 0.8997729 0.02201363 0.8566270 0.9429188</span></span> +<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="do">## 6: pca 1788 0.8996927 0.02245483 0.8556821 0.9437034</span></span></code></pre><p></p></div> <p>And the indices of prediction accuracy:</p> <p></p><div class="sourceCode r"><pre><code><span><span class="va">sc</span><span class="op">$</span><span class="va">Brier</span><span class="op">$</span><span class="va">score</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/order.html" class="external-link">order</a></span><span class="op">(</span><span class="op">-</span><span class="va">IPA</span><span class="op">)</span>, <span class="fu">.</span><span class="op">(</span><span class="va">model</span>, <span class="va">times</span>, <span class="va">IPA</span><span class="op">)</span><span class="op">]</span></span></code></pre><p></p></div> <p></p><div class="sourceCode"><pre><code><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="do">## model times IPA</span></span> @@ -564,11 +596,11 @@ <h4 id="linear-combinations-with-your-own-function">Linear combinations with you <span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="do">## 2: cph 1788 0.4759061</span></span> <span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="do">## 3: accel 1788 0.4743392</span></span> <span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="do">## 4: pca 1788 0.4398468</span></span> -<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="do">## 5: rando 1788 0.4219209</span></span> -<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="do">## 6: Null model 1788 0.0000000</span></span></code></pre><p></p></div> +<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="do">## 5: rlt 1788 0.4373910</span></span> +<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="do">## 6: rando 1788 0.4219209</span></span> +<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a><span class="do">## 7: Null model 1788 0.0000000</span></span></code></pre><p></p></div> <p>From inspection,</p><ul><li><p>the <code>glmnet</code> approach has the highest discrimination and index of prediction accuracy.</p></li> -<li><p>the accelerated ORSF is a close second.</p></li> <li><p>the random coefficients don’t do that well, but they aren’t bad.</p></li> </ul></div> @@ -659,29 +691,29 @@ <h4 id="comparing-orsf-with-other-learners">Comparing ORSF with other learners<a <span><span class="fu">glimpse</span><span class="op">(</span><span class="va">results</span><span class="op">)</span></span></code></pre><p></p></div> <p></p><div class="sourceCode"><pre><code><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="do">## Rows: 276</span></span> <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="do">## Columns: 23</span></span> -<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="do">## $ id <int> 8, 13, 31, 33, 35, 38, 83, 120, 127, 133, 143, 163, 165, 1~</span></span> -<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="do">## $ trt <fct> placebo, placebo, placebo, placebo, placebo, placebo, d_pe~</span></span> -<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="do">## $ age <dbl> 53.05681, 45.68925, 41.55236, 51.28268, 48.61875, 36.62697~</span></span> -<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="do">## $ sex <fct> f, f, f, f, f, f, f, m, f, m, f, f, m, f, f, f, f, f, f, f~</span></span> -<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="do">## $ ascites <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~</span></span> -<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a><span class="do">## $ hepato <fct> 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1~</span></span> -<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a><span class="do">## $ spiders <fct> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1~</span></span> -<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a><span class="do">## $ edema <fct> 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,~</span></span> -<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a><span class="do">## $ bili <dbl> 0.3, 0.7, 4.7, 0.8, 1.2, 3.3, 1.3, 3.5, 0.5, 1.5, 2.9, 0.3~</span></span> -<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a><span class="do">## $ chol <int> 280, 281, 296, 210, 314, 383, 250, 325, 268, 331, 332, 233~</span></span> -<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a><span class="do">## $ albumin <dbl> 4.00, 3.85, 3.44, 3.19, 3.20, 3.53, 3.50, 3.98, 4.08, 3.95~</span></span> -<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a><span class="do">## $ copper <int> 52, 40, 114, 82, 201, 102, 48, 444, 9, 13, 86, 20, 80, 67,~</span></span> -<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a><span class="do">## $ alk.phos <dbl> 4651.2, 1181.0, 9933.2, 1592.0, 12258.8, 1234.0, 1138.0, 7~</span></span> -<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a><span class="do">## $ ast <dbl> 28.38, 88.35, 206.40, 218.55, 72.24, 137.95, 71.30, 130.20~</span></span> -<span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a><span class="do">## $ trig <int> 189, 130, 101, 113, 151, 87, 100, 210, 95, 99, 103, 68, 14~</span></span> -<span id="cb1-18"><a href="#cb1-18" aria-hidden="true" tabindex="-1"></a><span class="do">## $ platelet <int> 373, 244, 195, 180, 431, 234, 81, 344, 453, 165, 277, 358,~</span></span> -<span id="cb1-19"><a href="#cb1-19" aria-hidden="true" tabindex="-1"></a><span class="do">## $ protime <dbl> 11.0, 10.6, 10.3, 12.0, 10.6, 11.0, 12.9, 10.6, 10.0, 10.1~</span></span> -<span id="cb1-20"><a href="#cb1-20" aria-hidden="true" tabindex="-1"></a><span class="do">## $ stage <ord> 3, 3, 2, 3, 3, 4, 4, 3, 2, 4, 4, 3, 4, 3, 2, 3, 4, 3, 3, 3~</span></span> -<span id="cb1-21"><a href="#cb1-21" aria-hidden="true" tabindex="-1"></a><span class="do">## $ time <int> 2466, 3577, 3839, 3170, 2847, 3244, 4050, 2033, 3255, 2796~</span></span> -<span id="cb1-22"><a href="#cb1-22" aria-hidden="true" tabindex="-1"></a><span class="do">## $ status <dbl> 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0~</span></span> -<span id="cb1-23"><a href="#cb1-23" aria-hidden="true" tabindex="-1"></a><span class="do">## $ pred_aorsf <dbl> 0.06002419, 0.01954988, 0.35024244, 0.29486541, 0.23418878~</span></span> -<span id="cb1-24"><a href="#cb1-24" aria-hidden="true" tabindex="-1"></a><span class="do">## $ pred_rfsrc <dbl> 0.052628661, 0.010204564, 0.401535927, 0.259857534, 0.3263~</span></span> -<span id="cb1-25"><a href="#cb1-25" aria-hidden="true" tabindex="-1"></a><span class="do">## $ pred_ranger <dbl> 0.040042884, 0.012915865, 0.392153766, 0.347688672, 0.3015~</span></span></code></pre><p></p></div> +<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="do">## $ id <int> 16, 29, 43, 62, 79, 82, 103, 105, 111, 114, 115, 139, 141,~</span></span> +<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="do">## $ trt <fct> placebo, placebo, d_penicill_main, placebo, d_penicill_mai~</span></span> +<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="do">## $ age <dbl> 40.44353, 63.87680, 48.87064, 60.70637, 46.51608, 67.31006~</span></span> +<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="do">## $ sex <fct> f, f, f, f, f, f, f, f, f, m, f, f, f, f, f, f, f, f, f, f~</span></span> +<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="do">## $ ascites <fct> 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0~</span></span> +<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a><span class="do">## $ hepato <fct> 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1~</span></span> +<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a><span class="do">## $ spiders <fct> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1~</span></span> +<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a><span class="do">## $ edema <fct> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~</span></span> +<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a><span class="do">## $ bili <dbl> 0.7, 0.7, 1.1, 1.3, 0.8, 4.5, 2.5, 1.1, 5.5, 3.2, 0.7, 1.1~</span></span> +<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a><span class="do">## $ chol <int> 204, 370, 361, 302, 315, 472, 188, 464, 528, 259, 303, 328~</span></span> +<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a><span class="do">## $ albumin <dbl> 3.66, 3.78, 3.64, 2.75, 4.24, 4.09, 3.67, 4.20, 4.18, 4.30~</span></span> +<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a><span class="do">## $ copper <int> 28, 24, 36, 58, 13, 154, 57, 38, 77, 208, 81, 159, 59, 76,~</span></span> +<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a><span class="do">## $ alk.phos <dbl> 685.0, 5833.0, 5430.2, 1523.0, 1637.0, 1580.0, 1273.0, 164~</span></span> +<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a><span class="do">## $ ast <dbl> 72.85, 73.53, 67.08, 43.40, 170.50, 117.80, 119.35, 151.90~</span></span> +<span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a><span class="do">## $ trig <int> 58, 86, 89, 112, 70, 272, 102, 102, 78, 78, 156, 134, 56, ~</span></span> +<span id="cb1-18"><a href="#cb1-18" aria-hidden="true" tabindex="-1"></a><span class="do">## $ platelet <int> 198, 390, 203, 329, 426, 412, 110, 348, 467, 268, 307, 142~</span></span> +<span id="cb1-19"><a href="#cb1-19" aria-hidden="true" tabindex="-1"></a><span class="do">## $ protime <dbl> 10.8, 10.6, 10.6, 13.2, 10.9, 11.1, 11.1, 10.3, 10.7, 11.7~</span></span> +<span id="cb1-20"><a href="#cb1-20" aria-hidden="true" tabindex="-1"></a><span class="do">## $ stage <ord> 3, 2, 2, 4, 3, 3, 4, 3, 3, 3, 3, 4, 2, 2, 3, 4, 2, 3, 4, 4~</span></span> +<span id="cb1-21"><a href="#cb1-21" aria-hidden="true" tabindex="-1"></a><span class="do">## $ time <int> 3672, 4509, 4556, 3090, 3707, 3574, 110, 3092, 2350, 3395,~</span></span> +<span id="cb1-22"><a href="#cb1-22" aria-hidden="true" tabindex="-1"></a><span class="do">## $ status <dbl> 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0~</span></span> +<span id="cb1-23"><a href="#cb1-23" aria-hidden="true" tabindex="-1"></a><span class="do">## $ pred_aorsf <dbl> 0.02210163, 0.12510110, 0.07571520, 0.59580668, 0.12839078~</span></span> +<span id="cb1-24"><a href="#cb1-24" aria-hidden="true" tabindex="-1"></a><span class="do">## $ pred_rfsrc <dbl> 0.01861595, 0.15632904, 0.07635485, 0.62281617, 0.19145913~</span></span> +<span id="cb1-25"><a href="#cb1-25" aria-hidden="true" tabindex="-1"></a><span class="do">## $ pred_ranger <dbl> 0.02143363, 0.13367920, 0.05892584, 0.54481330, 0.21380654~</span></span></code></pre><p></p></div> <p>And finish by aggregating the predictions and computing performance in the testing data. Note that I am computing one statistic for all predictions instead of computing one statistic for each fold. This @@ -702,16 +734,16 @@ <h4 id="comparing-orsf-with-other-learners">Comparing ORSF with other learners<a <span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="do">## Results by model:</span></span> <span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="do">## </span></span> <span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="do">## model times AUC lower upper</span></span> -<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="do">## 1: aorsf 1826 91.3 87.2 95.5</span></span> -<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a><span class="do">## 2: rfsrc 1826 90.0 85.8 94.3</span></span> -<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a><span class="do">## 3: ranger 1826 90.6 86.6 94.7</span></span> +<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="do">## 1: aorsf 1826 91.0 86.8 95.2</span></span> +<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a><span class="do">## 2: rfsrc 1826 89.2 84.8 93.7</span></span> +<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a><span class="do">## 3: ranger 1826 89.6 85.3 94.0</span></span> <span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a><span class="do">## </span></span> <span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a><span class="do">## Results of model comparisons:</span></span> <span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a><span class="do">## </span></span> -<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a><span class="do">## times model reference delta.AUC lower upper p</span></span> -<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a><span class="do">## 1: 1826 rfsrc aorsf -1.3 -2.9 0.3 0.1</span></span> -<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a><span class="do">## 2: 1826 ranger aorsf -0.7 -2.3 0.9 0.4</span></span> -<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a><span class="do">## 3: 1826 ranger rfsrc 0.6 -0.5 1.7 0.3</span></span> +<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a><span class="do">## times model reference delta.AUC lower upper p</span></span> +<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a><span class="do">## 1: 1826 rfsrc aorsf -1.7 -3.4 -0.1 0.04</span></span> +<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a><span class="do">## 2: 1826 ranger aorsf -1.3 -2.9 0.2 0.08</span></span> +<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a><span class="do">## 3: 1826 ranger rfsrc 0.4 -0.8 1.6 0.52</span></span> <span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a></span> <span id="cb1-18"><a href="#cb1-18" aria-hidden="true" tabindex="-1"></a><span class="do">## </span></span> <span id="cb1-19"><a href="#cb1-19" aria-hidden="true" tabindex="-1"></a><span class="do">## </span><span class="al">NOTE</span><span class="do">: Values are multiplied by 100 and given in %.</span></span> @@ -725,19 +757,19 @@ <h4 id="comparing-orsf-with-other-learners">Comparing ORSF with other learners<a <span id="cb1-27"><a href="#cb1-27" aria-hidden="true" tabindex="-1"></a><span class="do">## </span></span> <span id="cb1-28"><a href="#cb1-28" aria-hidden="true" tabindex="-1"></a><span class="do">## model times Brier lower upper IPA</span></span> <span id="cb1-29"><a href="#cb1-29" aria-hidden="true" tabindex="-1"></a><span class="do">## 1: Null model 1826.25 20.5 18.1 22.9 0.0</span></span> -<span id="cb1-30"><a href="#cb1-30" aria-hidden="true" tabindex="-1"></a><span class="do">## 2: aorsf 1826.25 10.6 8.5 12.8 48.0</span></span> -<span id="cb1-31"><a href="#cb1-31" aria-hidden="true" tabindex="-1"></a><span class="do">## 3: rfsrc 1826.25 11.8 9.7 13.9 42.4</span></span> -<span id="cb1-32"><a href="#cb1-32" aria-hidden="true" tabindex="-1"></a><span class="do">## 4: ranger 1826.25 11.5 9.5 13.5 44.0</span></span> +<span id="cb1-30"><a href="#cb1-30" aria-hidden="true" tabindex="-1"></a><span class="do">## 2: aorsf 1826.25 10.9 8.7 13.1 46.9</span></span> +<span id="cb1-31"><a href="#cb1-31" aria-hidden="true" tabindex="-1"></a><span class="do">## 3: rfsrc 1826.25 12.0 9.9 14.2 41.3</span></span> +<span id="cb1-32"><a href="#cb1-32" aria-hidden="true" tabindex="-1"></a><span class="do">## 4: ranger 1826.25 12.0 9.9 14.1 41.5</span></span> <span id="cb1-33"><a href="#cb1-33" aria-hidden="true" tabindex="-1"></a><span class="do">## </span></span> <span id="cb1-34"><a href="#cb1-34" aria-hidden="true" tabindex="-1"></a><span class="do">## Results of model comparisons:</span></span> <span id="cb1-35"><a href="#cb1-35" aria-hidden="true" tabindex="-1"></a><span class="do">## </span></span> <span id="cb1-36"><a href="#cb1-36" aria-hidden="true" tabindex="-1"></a><span class="do">## times model reference delta.Brier lower upper p</span></span> -<span id="cb1-37"><a href="#cb1-37" aria-hidden="true" tabindex="-1"></a><span class="do">## 1: 1826.25 aorsf Null model -9.8 -12.5 -7.2 1.872048e-13</span></span> -<span id="cb1-38"><a href="#cb1-38" aria-hidden="true" tabindex="-1"></a><span class="do">## 2: 1826.25 rfsrc Null model -8.7 -10.9 -6.5 2.176184e-14</span></span> -<span id="cb1-39"><a href="#cb1-39" aria-hidden="true" tabindex="-1"></a><span class="do">## 3: 1826.25 ranger Null model -9.0 -11.3 -6.7 1.387967e-14</span></span> -<span id="cb1-40"><a href="#cb1-40" aria-hidden="true" tabindex="-1"></a><span class="do">## 4: 1826.25 rfsrc aorsf 1.1 0.3 2.0 8.934160e-03</span></span> -<span id="cb1-41"><a href="#cb1-41" aria-hidden="true" tabindex="-1"></a><span class="do">## 5: 1826.25 ranger aorsf 0.8 0.1 1.6 3.287486e-02</span></span> -<span id="cb1-42"><a href="#cb1-42" aria-hidden="true" tabindex="-1"></a><span class="do">## 6: 1826.25 ranger rfsrc -0.3 -0.9 0.2 2.459287e-01</span></span> +<span id="cb1-37"><a href="#cb1-37" aria-hidden="true" tabindex="-1"></a><span class="do">## 1: 1826.25 aorsf Null model -9.6 -12.2 -7.0 9.364941e-13</span></span> +<span id="cb1-38"><a href="#cb1-38" aria-hidden="true" tabindex="-1"></a><span class="do">## 2: 1826.25 rfsrc Null model -8.5 -10.7 -6.2 2.074175e-13</span></span> +<span id="cb1-39"><a href="#cb1-39" aria-hidden="true" tabindex="-1"></a><span class="do">## 3: 1826.25 ranger Null model -8.5 -10.8 -6.2 3.712823e-13</span></span> +<span id="cb1-40"><a href="#cb1-40" aria-hidden="true" tabindex="-1"></a><span class="do">## 4: 1826.25 rfsrc aorsf 1.1 0.3 2.0 1.075856e-02</span></span> +<span id="cb1-41"><a href="#cb1-41" aria-hidden="true" tabindex="-1"></a><span class="do">## 5: 1826.25 ranger aorsf 1.1 0.3 1.9 4.825778e-03</span></span> +<span id="cb1-42"><a href="#cb1-42" aria-hidden="true" tabindex="-1"></a><span class="do">## 6: 1826.25 ranger rfsrc -0.1 -0.6 0.5 8.429772e-01</span></span> <span id="cb1-43"><a href="#cb1-43" aria-hidden="true" tabindex="-1"></a></span> <span id="cb1-44"><a href="#cb1-44" aria-hidden="true" tabindex="-1"></a><span class="do">## </span></span> <span id="cb1-45"><a href="#cb1-45" aria-hidden="true" tabindex="-1"></a><span class="do">## </span><span class="al">NOTE</span><span class="do">: Values are multiplied by 100 and given in %.</span></span> diff --git a/reference/orsf_control_custom.html b/reference/orsf_control_custom.html index b4238ecd..73363b63 100644 --- a/reference/orsf_control_custom.html +++ b/reference/orsf_control_custom.html @@ -123,7 +123,7 @@ <h3 id="random-coefficients">Random coefficients<a class="anchor" aria-label="an <span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a><span class="do">## Average leaves per tree: 20</span></span> <span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a><span class="do">## Min observations in leaf: 5</span></span> <span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a><span class="do">## Min events in leaf: 1</span></span> -<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a><span class="do">## OOB stat value: 0.83</span></span> +<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a><span class="do">## OOB stat value: 0.84</span></span> <span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a><span class="do">## OOB stat type: Harrell's C-statistic</span></span> <span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a><span class="do">## Variable importance: anova</span></span> <span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a><span class="do">## </span></span> @@ -157,9 +157,8 @@ <h3 id="evaluate">Evaluate<a class="anchor" aria-label="anchor" href="#evaluate" <p>How well do our two customized ORSFs do? Let’s compute their indices of prediction accuracy based on out-of-bag predictions:</p> -<p></p><div class="sourceCode r"><pre><code><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/tagteam/riskRegression" class="external-link">riskRegression</a></span><span class="op">)</span></span></code></pre><p></p></div> -<p></p><div class="sourceCode"><pre><code><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="do">## riskRegression version 2023.03.22</span></span></code></pre><p></p></div> -<p></p><div class="sourceCode r"><pre><code><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/therneau/survival" class="external-link">survival</a></span><span class="op">)</span></span> +<p></p><div class="sourceCode r"><pre><code><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/tagteam/riskRegression" class="external-link">riskRegression</a></span><span class="op">)</span></span> +<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/therneau/survival" class="external-link">survival</a></span><span class="op">)</span></span> <span></span> <span><span class="va">risk_preds</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>rando <span class="op">=</span> <span class="fl">1</span> <span class="op">-</span> <span class="va">fit_rando</span><span class="op">$</span><span class="va">pred_oobag</span>,</span> <span> pca <span class="op">=</span> <span class="fl">1</span> <span class="op">-</span> <span class="va">fit_pca</span><span class="op">$</span><span class="va">pred_oobag</span><span class="op">)</span></span> @@ -175,23 +174,21 @@ <h3 id="evaluate">Evaluate<a class="anchor" aria-label="anchor" href="#evaluate" <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="do">## Results by model:</span></span> <span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="do">## </span></span> <span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="do">## model times Brier lower upper IPA</span></span> -<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="do">## <fctr> <num> <char> <char> <char> <char></span></span> -<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="do">## 1: Null model 1788 20.479 18.090 22.868 0.000</span></span> -<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="do">## 2: rando 1788 11.672 9.596 13.748 43.006</span></span> -<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a><span class="do">## 3: pca 1788 12.917 10.885 14.950 36.924</span></span> -<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a><span class="do">## </span></span> -<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a><span class="do">## Results of model comparisons:</span></span> -<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a><span class="do">## </span></span> -<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a><span class="do">## times model reference delta.Brier lower upper p</span></span> -<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a><span class="do">## <num> <fctr> <fctr> <char> <char> <char> <num></span></span> -<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a><span class="do">## 1: 1788 rando Null model -8.807 -10.905 -6.709 1.896108e-16</span></span> -<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a><span class="do">## 2: 1788 pca Null model -7.562 -9.235 -5.888 8.331729e-19</span></span> -<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a><span class="do">## 3: 1788 pca rando 1.245 0.439 2.052 2.476657e-03</span></span> -<span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a></span> -<span id="cb1-18"><a href="#cb1-18" aria-hidden="true" tabindex="-1"></a><span class="do">## </span></span> -<span id="cb1-19"><a href="#cb1-19" aria-hidden="true" tabindex="-1"></a><span class="do">## </span><span class="al">NOTE</span><span class="do">: Values are multiplied by 100 and given in %.</span></span> -<span id="cb1-20"><a href="#cb1-20" aria-hidden="true" tabindex="-1"></a></span> -<span id="cb1-21"><a href="#cb1-21" aria-hidden="true" tabindex="-1"></a><span class="do">## </span><span class="al">NOTE</span><span class="do">: The lower Brier the better, the higher IPA the better.</span></span></code></pre><p></p></div> +<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="do">## 1: Null model 1788 20.479 18.090 22.868 0.000</span></span> +<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="do">## 2: rando 1788 11.554 9.476 13.631 43.584</span></span> +<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="do">## 3: pca 1788 12.673 10.692 14.654 38.118</span></span> +<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a><span class="do">## </span></span> +<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a><span class="do">## Results of model comparisons:</span></span> +<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a><span class="do">## </span></span> +<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a><span class="do">## times model reference delta.Brier lower upper p</span></span> +<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a><span class="do">## 1: 1788 rando Null model -8.926 -11.071 -6.780 3.491749e-16</span></span> +<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a><span class="do">## 2: 1788 pca Null model -7.806 -9.534 -6.079 8.192570e-19</span></span> +<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a><span class="do">## 3: 1788 pca rando 1.119 0.350 1.889 4.354090e-03</span></span> +<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a></span> +<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a><span class="do">## </span></span> +<span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a><span class="do">## </span><span class="al">NOTE</span><span class="do">: Values are multiplied by 100 and given in %.</span></span> +<span id="cb1-18"><a href="#cb1-18" aria-hidden="true" tabindex="-1"></a></span> +<span id="cb1-19"><a href="#cb1-19" aria-hidden="true" tabindex="-1"></a><span class="do">## </span><span class="al">NOTE</span><span class="do">: The lower Brier the better, the higher IPA the better.</span></span></code></pre><p></p></div> </div> </div> diff --git a/reference/orsf_control_net.html b/reference/orsf_control_net.html index 3b3d0a78..f0897fc4 100644 --- a/reference/orsf_control_net.html +++ b/reference/orsf_control_net.html @@ -125,10 +125,10 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e <span class="r-out co"><span class="r-pr">#></span> N trees: 25</span> <span class="r-out co"><span class="r-pr">#></span> N predictors total: 17</span> <span class="r-out co"><span class="r-pr">#></span> N predictors per node: 5</span> -<span class="r-out co"><span class="r-pr">#></span> Average leaves per tree: 25</span> +<span class="r-out co"><span class="r-pr">#></span> Average leaves per tree: 24</span> <span class="r-out co"><span class="r-pr">#></span> Min observations in leaf: 5</span> <span class="r-out co"><span class="r-pr">#></span> Min events in leaf: 1</span> -<span class="r-out co"><span class="r-pr">#></span> OOB stat value: 0.84</span> +<span class="r-out co"><span class="r-pr">#></span> OOB stat value: 0.83</span> <span class="r-out co"><span class="r-pr">#></span> OOB stat type: Harrell's C-statistic</span> <span class="r-out co"><span class="r-pr">#></span> Variable importance: anova</span> <span class="r-out co"><span class="r-pr">#></span> </span> diff --git a/reference/orsf_ice_oob.html b/reference/orsf_ice_oob.html index d4dd4133..0bbd7d80 100644 --- a/reference/orsf_ice_oob.html +++ b/reference/orsf_ice_oob.html @@ -227,19 +227,18 @@ <h2 id="examples">Examples<a class="anchor" aria-label="anchor" href="#examples" <span><span class="va">ice_oob</span> <span class="op"><-</span> <span class="fu"><a href="../reference/orsf_ice_oob.html">orsf_ice_oob</a></span><span class="op">(</span><span class="va">fit</span>, <span class="va">pred_spec</span>, boundary_checks <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span> <span></span> <span><span class="va">ice_oob</span></span></code></pre><p></p></div> -<p></p><div class="sourceCode"><pre><code><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="do">## id_variable id_row pred_horizon bili pred</span></span> -<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="do">## <int> <fctr> <num> <num> <num></span></span> -<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="do">## 1: 1 1 1788 1 0.9011797</span></span> -<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="do">## 2: 1 2 1788 1 0.1096207</span></span> -<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="do">## 3: 1 3 1788 1 0.7646444</span></span> -<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="do">## 4: 1 4 1788 1 0.3531060</span></span> -<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="do">## 5: 1 5 1788 1 0.1228441</span></span> -<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a><span class="do">## --- </span></span> -<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a><span class="do">## 6896: 25 272 1788 10 0.3089586</span></span> -<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a><span class="do">## 6897: 25 273 1788 10 0.4005430</span></span> -<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a><span class="do">## 6898: 25 274 1788 10 0.4933945</span></span> -<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a><span class="do">## 6899: 25 275 1788 10 0.3134373</span></span> -<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a><span class="do">## 6900: 25 276 1788 10 0.5002014</span></span></code></pre><p></p></div> +<p></p><div class="sourceCode"><pre><code><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="do">## id_variable id_row pred_horizon bili pred</span></span> +<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="do">## 1: 1 1 1788 1 0.9011797</span></span> +<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="do">## 2: 1 2 1788 1 0.1096207</span></span> +<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="do">## 3: 1 3 1788 1 0.7646444</span></span> +<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="do">## 4: 1 4 1788 1 0.3531060</span></span> +<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="do">## 5: 1 5 1788 1 0.1228441</span></span> +<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="do">## --- </span></span> +<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a><span class="do">## 6896: 25 272 1788 10 0.3089586</span></span> +<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a><span class="do">## 6897: 25 273 1788 10 0.4005430</span></span> +<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a><span class="do">## 6898: 25 274 1788 10 0.4933945</span></span> +<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a><span class="do">## 6899: 25 275 1788 10 0.3134373</span></span> +<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a><span class="do">## 6900: 25 276 1788 10 0.5002014</span></span></code></pre><p></p></div> <p>Much more detailed examples are given in the <a href="https://docs.ropensci.org/aorsf/articles/pd.html#individual-conditional-expectations-ice" class="external-link">vignette</a></p> </div> diff --git a/reference/orsf_pd_oob.html b/reference/orsf_pd_oob.html index 6255ce3e..75e92101 100644 --- a/reference/orsf_pd_oob.html +++ b/reference/orsf_pd_oob.html @@ -243,37 +243,34 @@ <h3 id="three-ways-to-compute-pd-and-ice">Three ways to compute PD and ICE<a cla <p></p><div class="sourceCode r"><pre><code><span><span class="va">pd_train</span> <span class="op"><-</span> <span class="fu"><a href="../reference/orsf_pd_oob.html">orsf_pd_inb</a></span><span class="op">(</span><span class="va">fit</span>, pred_spec <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>bili <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fl">5</span><span class="op">)</span><span class="op">)</span></span> <span></span> <span><span class="va">pd_train</span></span></code></pre><p></p></div> -<p></p><div class="sourceCode"><pre><code><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="do">## pred_horizon bili mean lwr medn upr</span></span> -<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="do">## <num> <num> <num> <num> <num> <num></span></span> -<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="do">## 1: 1826.25 1 0.2151663 0.02028479 0.09634648 0.7997269</span></span> -<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="do">## 2: 1826.25 2 0.2576618 0.03766695 0.15497447 0.8211875</span></span> -<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="do">## 3: 1826.25 3 0.2998484 0.06436773 0.20771324 0.8425637</span></span> -<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="do">## 4: 1826.25 4 0.3390664 0.08427149 0.25401067 0.8589590</span></span> -<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="do">## 5: 1826.25 5 0.3699045 0.10650098 0.28284427 0.8689855</span></span></code></pre><p></p></div></li> +<p></p><div class="sourceCode"><pre><code><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="do">## pred_horizon bili mean lwr medn upr</span></span> +<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="do">## 1: 1826.25 1 0.2151663 0.02028479 0.09634648 0.7997269</span></span> +<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="do">## 2: 1826.25 2 0.2576618 0.03766695 0.15497447 0.8211875</span></span> +<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="do">## 3: 1826.25 3 0.2998484 0.06436773 0.20771324 0.8425637</span></span> +<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="do">## 4: 1826.25 4 0.3390664 0.08427149 0.25401067 0.8589590</span></span> +<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="do">## 5: 1826.25 5 0.3699045 0.10650098 0.28284427 0.8689855</span></span></code></pre><p></p></div></li> <li><p>using out-of-bag predictions for the training data</p> <p></p><div class="sourceCode r"><pre><code><span><span class="va">pd_train</span> <span class="op"><-</span> <span class="fu"><a href="../reference/orsf_pd_oob.html">orsf_pd_oob</a></span><span class="op">(</span><span class="va">fit</span>, pred_spec <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>bili <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fl">5</span><span class="op">)</span><span class="op">)</span></span> <span></span> <span><span class="va">pd_train</span></span></code></pre><p></p></div> -<p></p><div class="sourceCode"><pre><code><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="do">## pred_horizon bili mean lwr medn upr</span></span> -<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="do">## <num> <num> <num> <num> <num> <num></span></span> -<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="do">## 1: 1826.25 1 0.2145044 0.01835000 0.09619052 0.7980629</span></span> -<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="do">## 2: 1826.25 2 0.2566241 0.03535358 0.14185734 0.8173143</span></span> -<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="do">## 3: 1826.25 3 0.2984693 0.05900059 0.20515477 0.8334243</span></span> -<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="do">## 4: 1826.25 4 0.3383547 0.07887323 0.24347513 0.8469769</span></span> -<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="do">## 5: 1826.25 5 0.3696260 0.10450534 0.28065473 0.8523756</span></span></code></pre><p></p></div></li> +<p></p><div class="sourceCode"><pre><code><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="do">## pred_horizon bili mean lwr medn upr</span></span> +<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="do">## 1: 1826.25 1 0.2145044 0.01835000 0.09619052 0.7980629</span></span> +<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="do">## 2: 1826.25 2 0.2566241 0.03535358 0.14185734 0.8173143</span></span> +<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="do">## 3: 1826.25 3 0.2984693 0.05900059 0.20515477 0.8334243</span></span> +<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="do">## 4: 1826.25 4 0.3383547 0.07887323 0.24347513 0.8469769</span></span> +<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="do">## 5: 1826.25 5 0.3696260 0.10450534 0.28065473 0.8523756</span></span></code></pre><p></p></div></li> <li><p>using predictions for a new set of data</p> <p></p><div class="sourceCode r"><pre><code><span><span class="va">pd_test</span> <span class="op"><-</span> <span class="fu"><a href="../reference/orsf_pd_oob.html">orsf_pd_new</a></span><span class="op">(</span><span class="va">fit</span>, </span> <span> new_data <span class="op">=</span> <span class="va">pbc_orsf_test</span>, </span> <span> pred_spec <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>bili <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fl">5</span><span class="op">)</span><span class="op">)</span></span> <span></span> <span><span class="va">pd_test</span></span></code></pre><p></p></div> -<p></p><div class="sourceCode"><pre><code><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="do">## pred_horizon bili mean lwr medn upr</span></span> -<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="do">## <num> <num> <num> <num> <num> <num></span></span> -<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="do">## 1: 1826.25 1 0.2542230 0.02901386 0.1943767 0.8143912</span></span> -<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="do">## 2: 1826.25 2 0.2955726 0.05037316 0.2474559 0.8317684</span></span> -<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="do">## 3: 1826.25 3 0.3388434 0.07453896 0.3010898 0.8488622</span></span> -<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="do">## 4: 1826.25 4 0.3800254 0.10565022 0.3516805 0.8592057</span></span> -<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="do">## 5: 1826.25 5 0.4124587 0.12292465 0.3915066 0.8690074</span></span></code></pre><p></p></div></li> +<p></p><div class="sourceCode"><pre><code><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="do">## pred_horizon bili mean lwr medn upr</span></span> +<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="do">## 1: 1826.25 1 0.2542230 0.02901386 0.1943767 0.8143912</span></span> +<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="do">## 2: 1826.25 2 0.2955726 0.05037316 0.2474559 0.8317684</span></span> +<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="do">## 3: 1826.25 3 0.3388434 0.07453896 0.3010898 0.8488622</span></span> +<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="do">## 4: 1826.25 4 0.3800254 0.10565022 0.3516805 0.8592057</span></span> +<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="do">## 5: 1826.25 5 0.4124587 0.12292465 0.3915066 0.8690074</span></span></code></pre><p></p></div></li> <li><p>in-bag partial dependence indicates relationships that the model has learned during training. This is helpful if your goal is to interpret the model.</p></li> diff --git a/reference/orsf_scale_cph.html b/reference/orsf_scale_cph.html index 5ef85390..f9c4269e 100644 --- a/reference/orsf_scale_cph.html +++ b/reference/orsf_scale_cph.html @@ -157,7 +157,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e <span class="r-in"><span></span></span> <span class="r-in"><span><span class="co"># numeric difference in x_mat and x_unscaled should be practically 0</span></span></span> <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">abs</a></span><span class="op">(</span><span class="va">x_mat</span> <span class="op">-</span> <span class="va">x_unscaled</span><span class="op">)</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> [1] 8.881784e-16</span> +<span class="r-out co"><span class="r-pr">#></span> [1] 3.552714e-15</span> </code></pre></div> </div> </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2> diff --git a/reference/orsf_summarize_uni.html b/reference/orsf_summarize_uni.html index 570e6e9b..72c8126d 100644 --- a/reference/orsf_summarize_uni.html +++ b/reference/orsf_summarize_uni.html @@ -151,24 +151,24 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> |---------------- risk ----------------|</span> <span class="r-out co"><span class="r-pr">#></span> Value Mean Median 25th % 75th %</span> -<span class="r-out co"><span class="r-pr">#></span> 0 0.3056596 0.1604080 0.05403967 0.5549432</span> -<span class="r-out co"><span class="r-pr">#></span> 1 0.4541163 0.3720453 0.24575548 0.6537995</span> +<span class="r-out co"><span class="r-pr">#></span> 0 0.3056310 0.1674688 0.05252807 0.5526941</span> +<span class="r-out co"><span class="r-pr">#></span> 1 0.4442562 0.3536698 0.22121860 0.6347482</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> -- edema (VI Rank: 2) --------------------------</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> |---------------- risk ----------------|</span> <span class="r-out co"><span class="r-pr">#></span> Value Mean Median 25th % 75th %</span> -<span class="r-out co"><span class="r-pr">#></span> 0 0.3006362 0.1597512 0.05229420 0.5519929</span> -<span class="r-out co"><span class="r-pr">#></span> 0.5 0.3623431 0.2439360 0.09813076 0.6174363</span> -<span class="r-out co"><span class="r-pr">#></span> 1 0.4563040 0.3679923 0.24789758 0.6588647</span> +<span class="r-out co"><span class="r-pr">#></span> 0 0.2995075 0.1657309 0.05169169 0.5528988</span> +<span class="r-out co"><span class="r-pr">#></span> 0.5 0.3625881 0.2581942 0.10846891 0.6305115</span> +<span class="r-out co"><span class="r-pr">#></span> 1 0.4564929 0.3715971 0.24401693 0.6522910</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> -- bili (VI Rank: 3) ---------------------------</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> |---------------- risk ----------------|</span> <span class="r-out co"><span class="r-pr">#></span> Value Mean Median 25th % 75th %</span> -<span class="r-out co"><span class="r-pr">#></span> 0.80 0.2396105 0.1322078 0.05552047 0.3900583</span> -<span class="r-out co"><span class="r-pr">#></span> 1.4 0.2667532 0.1579549 0.07450011 0.4186372</span> -<span class="r-out co"><span class="r-pr">#></span> 3.5 0.3754583 0.2833106 0.16142246 0.5699637</span> +<span class="r-out co"><span class="r-pr">#></span> 0.80 0.2401344 0.1335075 0.05493055 0.3787130</span> +<span class="r-out co"><span class="r-pr">#></span> 1.4 0.2665132 0.1618601 0.06895290 0.4172400</span> +<span class="r-out co"><span class="r-pr">#></span> 3.5 0.3704224 0.2788340 0.15248087 0.5542718</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Predicted risk at time t = 1788 for top 3 predictors </span> <span class="r-in"><span></span></span> @@ -182,24 +182,24 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> |---------------- risk ----------------|</span> <span class="r-out co"><span class="r-pr">#></span> Value Mean Median 25th % 75th %</span> -<span class="r-out co"><span class="r-pr">#></span> 0.80 0.2396105 0.1322078 0.05552047 0.3900583</span> -<span class="r-out co"><span class="r-pr">#></span> 1.4 0.2667532 0.1579549 0.07450011 0.4186372</span> -<span class="r-out co"><span class="r-pr">#></span> 3.5 0.3754583 0.2833106 0.16142246 0.5699637</span> +<span class="r-out co"><span class="r-pr">#></span> 0.80 0.2401344 0.1335075 0.05493055 0.3787130</span> +<span class="r-out co"><span class="r-pr">#></span> 1.4 0.2665132 0.1618601 0.06895290 0.4172400</span> +<span class="r-out co"><span class="r-pr">#></span> 3.5 0.3704224 0.2788340 0.15248087 0.5542718</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> -- copper (VI Rank: 2) -------------------------</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> |---------------- risk ----------------|</span> <span class="r-out co"><span class="r-pr">#></span> Value Mean Median 25th % 75th %</span> -<span class="r-out co"><span class="r-pr">#></span> 43 0.2703663 0.1485739 0.05372852 0.4547391</span> -<span class="r-out co"><span class="r-pr">#></span> 74 0.2964628 0.1714574 0.06789143 0.4963866</span> -<span class="r-out co"><span class="r-pr">#></span> 129 0.3534530 0.2487902 0.12636325 0.5775581</span> +<span class="r-out co"><span class="r-pr">#></span> 43 0.2698245 0.1495333 0.05165107 0.4526158</span> +<span class="r-out co"><span class="r-pr">#></span> 74 0.2958194 0.1690855 0.07162885 0.4905227</span> +<span class="r-out co"><span class="r-pr">#></span> 129 0.3513684 0.2418974 0.12093492 0.5513119</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> -- sex (VI Rank: 3) ----------------------------</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> |---------------- risk ----------------|</span> -<span class="r-out co"><span class="r-pr">#></span> Value Mean Median 25th % 75th %</span> -<span class="r-out co"><span class="r-pr">#></span> m 0.3666038 0.2542865 0.1260373 0.5933671</span> -<span class="r-out co"><span class="r-pr">#></span> f 0.3048034 0.1532173 0.0522942 0.5401139</span> +<span class="r-out co"><span class="r-pr">#></span> Value Mean Median 25th % 75th %</span> +<span class="r-out co"><span class="r-pr">#></span> m 0.3606311 0.2502953 0.11850705 0.5863728</span> +<span class="r-out co"><span class="r-pr">#></span> f 0.3041468 0.1632798 0.05252807 0.5341361</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Predicted risk at time t = 1788 for top 3 predictors </span> <span class="r-in"><span></span></span> diff --git a/reference/orsf_time_to_train.html b/reference/orsf_time_to_train.html index b7fbfc97..e609e50b 100644 --- a/reference/orsf_time_to_train.html +++ b/reference/orsf_time_to_train.html @@ -94,7 +94,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e <span class="r-in"><span><span class="va">time_estimated</span> <span class="op"><-</span> <span class="fu">orsf_time_to_train</span><span class="op">(</span><span class="va">object</span>, n_tree_subset <span class="op">=</span> <span class="fl">50</span><span class="op">)</span></span></span> <span class="r-in"><span></span></span> <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">time_estimated</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> Time difference of 0.2371516 secs</span> +<span class="r-out co"><span class="r-pr">#></span> Time difference of 0.2824929 secs</span> <span class="r-in"><span></span></span> <span class="r-in"><span><span class="co"># let's see how close the approximation was</span></span></span> <span class="r-in"><span><span class="va">time_true_start</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/Sys.time.html" class="external-link">Sys.time</a></span><span class="op">(</span><span class="op">)</span></span></span> @@ -104,11 +104,11 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e <span class="r-in"><span><span class="va">time_true</span> <span class="op"><-</span> <span class="va">time_true_stop</span> <span class="op">-</span> <span class="va">time_true_start</span></span></span> <span class="r-in"><span></span></span> <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">time_true</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> Time difference of 0.2216668 secs</span> +<span class="r-out co"><span class="r-pr">#></span> Time difference of 0.2648766 secs</span> <span class="r-in"><span></span></span> <span class="r-in"><span><span class="co"># error</span></span></span> <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">abs</a></span><span class="op">(</span><span class="va">time_true</span> <span class="op">-</span> <span class="va">time_estimated</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> Time difference of 0.01548481 secs</span> +<span class="r-out co"><span class="r-pr">#></span> Time difference of 0.01761627 secs</span> <span class="r-in"><span></span></span> </code></pre></div> </div> diff --git a/reference/orsf_vs.html b/reference/orsf_vs.html index 43f780b3..9a062c74 100644 --- a/reference/orsf_vs.html +++ b/reference/orsf_vs.html @@ -105,35 +105,35 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e <span class="r-in"><span> tree_seeds <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fl">25</span><span class="op">)</span></span></span> <span class="r-in"><span></span></span> <span class="r-in"><span><span class="fu">orsf_vs</span><span class="op">(</span><span class="va">object</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> n_predictors stat_value predictors_included</span> -<span class="r-out co"><span class="r-pr">#></span> 1: 3 0.7934788 ascites,edema,bili</span> -<span class="r-out co"><span class="r-pr">#></span> 2: 4 0.8156154 age,ascites,edema,bili</span> -<span class="r-out co"><span class="r-pr">#></span> 3: 5 0.8205115 age,ascites,edema,bili,copper</span> -<span class="r-out co"><span class="r-pr">#></span> 4: 6 0.8297568 age,ascites,edema,bili,chol,copper</span> -<span class="r-out co"><span class="r-pr">#></span> 5: 7 0.8189749 age,ascites,hepato,edema,bili,chol,...</span> -<span class="r-out co"><span class="r-pr">#></span> 6: 8 0.8296526 age,ascites,hepato,spiders,edema,bili,...</span> -<span class="r-out co"><span class="r-pr">#></span> 7: 9 0.8257201 age,sex,ascites,hepato,spiders,edema,...</span> -<span class="r-out co"><span class="r-pr">#></span> 8: 10 0.8252253 age,sex,ascites,hepato,spiders,edema,...</span> -<span class="r-out co"><span class="r-pr">#></span> 9: 11 0.8225168 age,sex,ascites,hepato,spiders,edema,...</span> -<span class="r-out co"><span class="r-pr">#></span> 10: 12 0.8095474 age,sex,ascites,hepato,spiders,edema,...</span> -<span class="r-out co"><span class="r-pr">#></span> 11: 13 0.8236887 age,sex,ascites,hepato,spiders,edema,...</span> -<span class="r-out co"><span class="r-pr">#></span> 12: 14 0.8277514 age,sex,ascites,hepato,spiders,edema,...</span> -<span class="r-out co"><span class="r-pr">#></span> 13: 15 0.8134017 age,sex,ascites,hepato,spiders,edema,...</span> -<span class="r-out co"><span class="r-pr">#></span> 14: 16 0.8393666 age,sex,ascites,hepato,spiders,edema,...</span> -<span class="r-out co"><span class="r-pr">#></span> 15: 17 0.8200167 id,age,sex,ascites,hepato,spiders,...</span> -<span class="r-out co"><span class="r-pr">#></span> 16: 18 0.8119954 id,trt,age,sex,ascites,hepato,...</span> +<span class="r-out co"><span class="r-pr">#></span> n_predictors stat_value predictors_included</span> +<span class="r-out co"><span class="r-pr">#></span> 1: 3 0.7937393 ascites,edema,bili</span> +<span class="r-out co"><span class="r-pr">#></span> 2: 4 0.8159279 age,ascites,edema,bili</span> +<span class="r-out co"><span class="r-pr">#></span> 3: 5 0.8205115 age,ascites,edema,bili,copper</span> +<span class="r-out co"><span class="r-pr">#></span> 4: 6 0.8316839 age,ascites,edema,bili,albumin,copper</span> +<span class="r-out co"><span class="r-pr">#></span> 5: 7 0.8346008 age,ascites,edema,bili,chol,albumin,...</span> +<span class="r-out co"><span class="r-pr">#></span> 6: 8 0.8277254 age,ascites,edema,bili,chol,albumin,...</span> +<span class="r-out co"><span class="r-pr">#></span> 7: 9 0.8322048 age,ascites,edema,bili,chol,albumin,...</span> +<span class="r-out co"><span class="r-pr">#></span> 8: 10 0.8252253 age,ascites,edema,bili,chol,albumin,...</span> +<span class="r-out co"><span class="r-pr">#></span> 9: 11 0.8277775 age,sex,ascites,edema,bili,chol,...</span> +<span class="r-out co"><span class="r-pr">#></span> 10: 12 0.8199385 age,sex,ascites,hepato,edema,bili,...</span> +<span class="r-out co"><span class="r-pr">#></span> 11: 13 0.8214490 age,sex,ascites,hepato,spiders,edema,...</span> +<span class="r-out co"><span class="r-pr">#></span> 12: 14 0.8298088 age,sex,ascites,hepato,spiders,edema,...</span> +<span class="r-out co"><span class="r-pr">#></span> 13: 15 0.8151206 age,sex,ascites,hepato,spiders,edema,...</span> +<span class="r-out co"><span class="r-pr">#></span> 14: 16 0.8391062 age,sex,ascites,hepato,spiders,edema,...</span> +<span class="r-out co"><span class="r-pr">#></span> 15: 17 0.8200167 id,age,sex,ascites,hepato,spiders,...</span> +<span class="r-out co"><span class="r-pr">#></span> 16: 18 0.8105370 id,trt,age,sex,ascites,hepato,...</span> <span class="r-out co"><span class="r-pr">#></span> predictor_dropped</span> <span class="r-out co"><span class="r-pr">#></span> 1: ascites</span> <span class="r-out co"><span class="r-pr">#></span> 2: age</span> <span class="r-out co"><span class="r-pr">#></span> 3: copper</span> -<span class="r-out co"><span class="r-pr">#></span> 4: chol</span> -<span class="r-out co"><span class="r-pr">#></span> 5: hepato</span> -<span class="r-out co"><span class="r-pr">#></span> 6: spiders</span> -<span class="r-out co"><span class="r-pr">#></span> 7: sex</span> -<span class="r-out co"><span class="r-pr">#></span> 8: albumin</span> -<span class="r-out co"><span class="r-pr">#></span> 9: ast</span> -<span class="r-out co"><span class="r-pr">#></span> 10: protime</span> -<span class="r-out co"><span class="r-pr">#></span> 11: alk.phos</span> +<span class="r-out co"><span class="r-pr">#></span> 4: albumin</span> +<span class="r-out co"><span class="r-pr">#></span> 5: chol</span> +<span class="r-out co"><span class="r-pr">#></span> 6: protime</span> +<span class="r-out co"><span class="r-pr">#></span> 7: ast</span> +<span class="r-out co"><span class="r-pr">#></span> 8: alk.phos</span> +<span class="r-out co"><span class="r-pr">#></span> 9: sex</span> +<span class="r-out co"><span class="r-pr">#></span> 10: hepato</span> +<span class="r-out co"><span class="r-pr">#></span> 11: spiders</span> <span class="r-out co"><span class="r-pr">#></span> 12: stage</span> <span class="r-out co"><span class="r-pr">#></span> 13: platelet</span> <span class="r-out co"><span class="r-pr">#></span> 14: trig</span> diff --git a/reference/print.orsf_fit.html b/reference/print.orsf_fit.html index 91a06ab4..0ae74359 100644 --- a/reference/print.orsf_fit.html +++ b/reference/print.orsf_fit.html @@ -135,10 +135,10 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e <span class="r-out co"><span class="r-pr">#></span> N trees: 5</span> <span class="r-out co"><span class="r-pr">#></span> N predictors total: 17</span> <span class="r-out co"><span class="r-pr">#></span> N predictors per node: 5</span> -<span class="r-out co"><span class="r-pr">#></span> Average leaves per tree: 25</span> +<span class="r-out co"><span class="r-pr">#></span> Average leaves per tree: 27</span> <span class="r-out co"><span class="r-pr">#></span> Min observations in leaf: 5</span> <span class="r-out co"><span class="r-pr">#></span> Min events in leaf: 1</span> -<span class="r-out co"><span class="r-pr">#></span> OOB stat value: 0.74</span> +<span class="r-out co"><span class="r-pr">#></span> OOB stat value: 0.76</span> <span class="r-out co"><span class="r-pr">#></span> OOB stat type: Harrell's C-statistic</span> <span class="r-out co"><span class="r-pr">#></span> Variable importance: anova</span> <span class="r-out co"><span class="r-pr">#></span> </span> diff --git a/reference/print.orsf_summary_uni.html b/reference/print.orsf_summary_uni.html index 06276f39..d993f13c 100644 --- a/reference/print.orsf_summary_uni.html +++ b/reference/print.orsf_summary_uni.html @@ -101,24 +101,24 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> |---------------- risk ----------------|</span> <span class="r-out co"><span class="r-pr">#></span> Value Mean Median 25th % 75th %</span> -<span class="r-out co"><span class="r-pr">#></span> 0.80 0.2372302 0.1290978 0.04988573 0.3759563</span> -<span class="r-out co"><span class="r-pr">#></span> 1.4 0.2637580 0.1564218 0.06983125 0.4189478</span> -<span class="r-out co"><span class="r-pr">#></span> 3.5 0.3769927 0.2961613 0.17078560 0.5771778</span> +<span class="r-out co"><span class="r-pr">#></span> 0.80 0.2339194 0.1221911 0.05320592 0.3542689</span> +<span class="r-out co"><span class="r-pr">#></span> 1.4 0.2633691 0.1536543 0.07504906 0.4054625</span> +<span class="r-out co"><span class="r-pr">#></span> 3.5 0.3805454 0.2979334 0.16496936 0.5694776</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> -- copper (VI Rank: 2) -------------------------</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> |---------------- risk ----------------|</span> <span class="r-out co"><span class="r-pr">#></span> Value Mean Median 25th % 75th %</span> -<span class="r-out co"><span class="r-pr">#></span> 43 0.2687370 0.1562238 0.05255161 0.4302762</span> -<span class="r-out co"><span class="r-pr">#></span> 74 0.2906106 0.1715181 0.06366127 0.4734150</span> -<span class="r-out co"><span class="r-pr">#></span> 129 0.3488795 0.2561287 0.11884521 0.5729055</span> +<span class="r-out co"><span class="r-pr">#></span> 43 0.2721538 0.1431253 0.05662816 0.4678858</span> +<span class="r-out co"><span class="r-pr">#></span> 74 0.2959924 0.1676301 0.06786956 0.5000693</span> +<span class="r-out co"><span class="r-pr">#></span> 129 0.3494525 0.2324844 0.11008935 0.5605183</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> -- sex (VI Rank: 3) ----------------------------</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> |---------------- risk ----------------|</span> <span class="r-out co"><span class="r-pr">#></span> Value Mean Median 25th % 75th %</span> -<span class="r-out co"><span class="r-pr">#></span> m 0.3638092 0.2579718 0.12172678 0.5956792</span> -<span class="r-out co"><span class="r-pr">#></span> f 0.3042276 0.1688181 0.05253388 0.5343637</span> +<span class="r-out co"><span class="r-pr">#></span> m 0.3602617 0.2525631 0.11691691 0.5969917</span> +<span class="r-out co"><span class="r-pr">#></span> f 0.3068573 0.1538510 0.05301943 0.5580260</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Predicted risk at time t = 1788 for top 3 predictors </span> <span class="r-in"><span></span></span> diff --git a/search.json b/search.json index 35064dbf..f591bc37 100644 --- a/search.json +++ b/search.json @@ -1 +1 @@ -[{"path":"https://bcjaeger.github.io/aorsf/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to aorsf","title":"Contributing to aorsf","text":"Want contribute aorsf? Great! aorsf initially stable state development, great deal active subsequent development envisioned. outline propose change aorsf. detailed info contributing , tidyverse packages, please see development contributing guide.","code":""},{"path":"https://bcjaeger.github.io/aorsf/CONTRIBUTING.html","id":"fixing-typos","dir":"","previous_headings":"","what":"Fixing typos","title":"Contributing to aorsf","text":"can fix typos, spelling mistakes, grammatical errors documentation directly using GitHub web interface, long changes made source file. generally means ’ll need edit roxygen2 comments .R, .Rd file. can find .R file generates .Rd reading comment first line.","code":""},{"path":"https://bcjaeger.github.io/aorsf/CONTRIBUTING.html","id":"bigger-changes","dir":"","previous_headings":"","what":"Bigger changes","title":"Contributing to aorsf","text":"want make bigger change, ’s good idea first file issue make sure someone team agrees ’s needed. ’ve found bug, please file issue illustrates bug minimal reprex (also help write unit test, needed).","code":""},{"path":"https://bcjaeger.github.io/aorsf/CONTRIBUTING.html","id":"pull-request-process","dir":"","previous_headings":"Bigger changes","what":"Pull request process","title":"Contributing to aorsf","text":"Fork package clone onto computer. haven’t done , recommend using usethis::create_from_github(\"ropensci/aorsf\", fork = TRUE). Install development dependencies devtools::install_dev_deps(), make sure package passes R CMD check running devtools::check(). R CMD check doesn’t pass cleanly, ’s good idea ask help continuing. Create Git branch pull request (PR). recommend using usethis::pr_init(\"brief-description--change\"). Make changes, commit git, create PR running usethis::pr_push(), following prompts browser. title PR briefly describe change. body PR contain Fixes #issue-number. user-facing changes, add bullet top NEWS.md (.e. just first header). Follow style described https://style.tidyverse.org/news.html.","code":""},{"path":"https://bcjaeger.github.io/aorsf/CONTRIBUTING.html","id":"code-style","dir":"","previous_headings":"Bigger changes","what":"Code style","title":"Contributing to aorsf","text":"New code follow tidyverse style guide. can use styler package apply styles, please don’t restyle code nothing PR. use roxygen2, Markdown syntax, documentation. use testthat unit tests. Contributions test cases included easier accept.","code":""},{"path":"https://bcjaeger.github.io/aorsf/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contributing to aorsf","text":"Please note aorsf project released Contributor Code Conduct. contributing project agree abide terms.","code":""},{"path":"https://bcjaeger.github.io/aorsf/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2022 aorsf authors (Byron C. Jaeger, Sawyer Welden, Nicholas M. Pajewski) Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://bcjaeger.github.io/aorsf/articles/aorsf.html","id":"background-orsf","dir":"Articles","previous_headings":"","what":"Background: ORSF","title":"Introduction to aorsf","text":"oblique random survival forest (ORSF) extension axis-based RSF algorithm. See orsf details ORSFs. see arXiv paper details algorithms used specifically aorsf.","code":""},{"path":"https://bcjaeger.github.io/aorsf/articles/aorsf.html","id":"accelerated-orsf","dir":"Articles","previous_headings":"","what":"Accelerated ORSF","title":"Introduction to aorsf","text":"purpose aorsf (‘’ short accelerated) provide routines fit ORSFs scale adequately large data sets. fastest algorithm available package accelerated ORSF model, default method used orsf(): may notice first input aorsf data. design choice makes easier use orsf pipes (.e., %>% |>). instance,","code":"library(aorsf) set.seed(329) orsf_fit <- orsf(data = pbc_orsf, formula = Surv(time, status) ~ . - id) orsf_fit #> ---------- Oblique random survival forest #> #> Linear combinations: Accelerated #> N observations: 276 #> N events: 111 #> N trees: 500 #> N predictors total: 17 #> N predictors per node: 5 #> Average leaves per tree: 25 #> Min observations in leaf: 5 #> Min events in leaf: 1 #> OOB stat value: 0.84 #> OOB stat type: Harrell's C-statistic #> Variable importance: anova #> #> ----------------------------------------- library(dplyr) orsf_fit <- pbc_orsf |> select(-id) |> orsf(formula = Surv(time, status) ~ .)"},{"path":"https://bcjaeger.github.io/aorsf/articles/aorsf.html","id":"interpretation","dir":"Articles","previous_headings":"","what":"Interpretation","title":"Introduction to aorsf","text":"aorsf includes several functions dedicated interpretation ORSFs, estimation partial dependence variable importance.","code":""},{"path":"https://bcjaeger.github.io/aorsf/articles/aorsf.html","id":"variable-importance","dir":"Articles","previous_headings":"Interpretation","what":"Variable importance","title":"Introduction to aorsf","text":"aorsf provides multiple ways compute variable importance. compute negation importance, ORSF multiplies coefficient variable -1 re-computes --sample (sometimes referred --bag) accuracy ORSF model. can also compute variable importance using permutation, classical approach. faster alternative permutation negation importance ANOVA importance, computes proportion times variable obtains low p-value (p < 0.01) forest grown.","code":"orsf_vi_negate(orsf_fit) #> bili copper protime stage sex #> 1.129201e-01 5.143202e-02 2.985467e-02 2.913153e-02 2.648666e-02 #> age albumin ascites ast chol #> 2.257257e-02 2.222867e-02 1.560638e-02 1.231634e-02 1.203531e-02 #> edema trt hepato spiders trig #> 9.463853e-03 7.772744e-03 6.663188e-03 6.162035e-03 5.138559e-03 #> alk.phos platelet #> 3.549245e-03 -6.782850e-06 orsf_vi_permute(orsf_fit) #> bili copper protime albumin age #> 0.0507342790 0.0239177369 0.0163535749 0.0125096251 0.0119269625 #> stage ascites ast edema chol #> 0.0115229977 0.0104655581 0.0077846459 0.0058569776 0.0048923838 #> spiders hepato sex trig alk.phos #> 0.0035124258 0.0034740982 0.0021089828 0.0019406658 0.0011960153 #> platelet trt #> -0.0004113343 -0.0008678193 orsf_vi_anova(orsf_fit) #> ascites edema bili copper albumin age protime spiders #> 0.4248497 0.2966406 0.2962241 0.2200782 0.2056028 0.2044682 0.1952912 0.1701295 #> chol ast stage hepato sex trig alk.phos platelet #> 0.1698671 0.1575173 0.1568678 0.1479081 0.1460362 0.1277078 0.1160302 0.1098039 #> trt #> 0.1094891"},{"path":"https://bcjaeger.github.io/aorsf/articles/aorsf.html","id":"partial-dependence-pd","dir":"Articles","previous_headings":"Interpretation","what":"Partial dependence (PD)","title":"Introduction to aorsf","text":"Partial dependence (PD) shows expected prediction model function single predictor multiple predictors. expectation marginalized values predictors, giving something like multivariable adjusted estimate model’s prediction. PD, see vignette","code":""},{"path":"https://bcjaeger.github.io/aorsf/articles/aorsf.html","id":"individual-conditional-expectations-ice","dir":"Articles","previous_headings":"Interpretation","what":"Individual conditional expectations (ICE)","title":"Introduction to aorsf","text":"Unlike partial dependence, shows expected prediction function one multiple predictors, individual conditional expectations (ICE) show prediction individual observation function predictor. ICE, see vignette","code":""},{"path":"https://bcjaeger.github.io/aorsf/articles/aorsf.html","id":"what-about-the-original-orsf","dir":"Articles","previous_headings":"","what":"What about the original ORSF?","title":"Introduction to aorsf","text":"original ORSF (.e., obliqueRSF) used glmnet find linear combinations inputs. aorsf allows users implement approach using orsf_control_net() function: net forests fit lot faster original ORSF function obliqueRSF. However, net forests still much slower cph ones:","code":"orsf_net <- orsf(data = pbc_orsf, formula = Surv(time, status) ~ . - id, control = orsf_control_net(), n_tree = 50) # tracking how long it takes to fit 50 glmnet trees print( t1 <- system.time( orsf(data = pbc_orsf, formula = Surv(time, status) ~ . - id, control = orsf_control_net(), n_tree = 50) ) ) #> user system elapsed #> 5.129 0.000 5.130 # and how long it takes to fit 50 cph trees print( t2 <- system.time( orsf(data = pbc_orsf, formula = Surv(time, status) ~ . - id, control = orsf_control_cph(), n_tree = 50) ) ) #> user system elapsed #> 0.053 0.000 0.053 t1['elapsed'] / t2['elapsed'] #> elapsed #> 96.79245"},{"path":"https://bcjaeger.github.io/aorsf/articles/aorsf.html","id":"aorsf-and-other-machine-learning-software","dir":"Articles","previous_headings":"","what":"aorsf and other machine learning software","title":"Introduction to aorsf","text":"unique feature aorsf fast algorithms fit ORSF ensembles. RLT obliqueRSF fit oblique random survival forests, aorsf faster. ranger randomForestSRC fit survival forests, neither package supports oblique splitting. obliqueRF fits oblique random forests classification regression, survival. PPforest fits oblique random forests classification survival. Note: default prediction behavior aorsf models produce predicted risk specific prediction horizon, default ranger randomForestSRC. think change future, computing time independent predictions aorsf helpful.","code":""},{"path":"https://bcjaeger.github.io/aorsf/articles/oobag.html","id":"out-of-bag-data","dir":"Articles","previous_headings":"","what":"Out-of-bag data","title":"Out-of-bag predictions and evaluation","text":"random forests, tree grown bootstrapped version training set. bootstrap samples selected replacement, bootstrapped training set contains two-thirds instances original training set. ‘--bag’ data instances bootstrapped training set.","code":""},{"path":"https://bcjaeger.github.io/aorsf/articles/oobag.html","id":"out-of-bag-predictions-and-error","dir":"Articles","previous_headings":"","what":"Out-of-bag predictions and error","title":"Out-of-bag predictions and evaluation","text":"tree random forest can make predictions --bag data, --bag predictions can aggregated make ensemble --bag prediction. Since --bag data used grow tree, accuracy ensemble --bag predictions approximate generalization error random forest. --bag prediction error plays central role routines estimate variable importance, e.g. negation importance. Let’s fit oblique random survival forest plot distribution ensemble --bag predictions. surprisingly, survival predictions 0 1. Next, let’s check --bag accuracy fit: --bag estimate Harrell’s C-statistic (default method evaluate --bag predictions) 0.8404084.","code":"fit <- orsf(data = pbc_orsf, formula = Surv(time, status) ~ . - id, oobag_pred_type = 'surv', oobag_pred_horizon = 2000) hist(fit$pred_oobag, main = 'Ensemble out-of-bag survival predictions at t=3,500') # what function is used to evaluate out-of-bag predictions? fit$eval_oobag$stat_type #> [1] \"Harrell's C-statistic\" # what is the output from this function? fit$eval_oobag$stat_values #> [,1] #> [1,] 0.8404084"},{"path":"https://bcjaeger.github.io/aorsf/articles/oobag.html","id":"monitoring-out-of-bag-error","dir":"Articles","previous_headings":"","what":"Monitoring out-of-bag error","title":"Out-of-bag predictions and evaluation","text":"--bag data set contains one-third training set, --bag error estimate usually converges stable value trees added forest. want monitor convergence --bag error oblique random survival forest, can set oobag_eval_every compute --bag error every oobag_eval_every tree. example, let’s compute --bag error fitting tree forest 50 trees: general, least 500 trees recommended random forest fit. ’re just using 50 case better illustration --bag error curve. Also, helps make run-times low whenever need re-compile package vignettes.","code":"fit <- orsf(data = pbc_orsf, formula = Surv(time, status) ~ . - id, n_tree = 50, oobag_pred_type = 'surv', oobag_pred_horizon = 2000, oobag_eval_every = 1) plot( x = seq(1, 50, by = 1), y = fit$eval_oobag$stat_values, main = 'Out-of-bag C-statistic computed after each new tree is grown.', xlab = 'Number of trees grown', ylab = fit$eval_oobag$stat_type )"},{"path":"https://bcjaeger.github.io/aorsf/articles/oobag.html","id":"user-supplied-out-of-bag-evaluation-functions","dir":"Articles","previous_headings":"","what":"User-supplied out-of-bag evaluation functions","title":"Out-of-bag predictions and evaluation","text":"cases, may want control --bag error estimated. example, let’s use Brier score SurvMetrics package: two ways apply function compute --bag error. First, can apply function --bag survival predictions stored ‘aorsf’ objects, e.g: Second, can pass function orsf(), used place Harrell’s C-statistic: can also compute time-dependent C-statistic instead Harrell’s C-statistic (default oob function):","code":"oobag_fun_brier <- function(y_mat, w_vec, s_vec){ # output is numeric vector of length 1 as.numeric( SurvMetrics::Brier( object = Surv(time = y_mat[, 1], event = y_mat[, 2]), pre_sp = s_vec, # t_star in Brier() should match oob_pred_horizon in orsf() t_star = 2000 ) ) } oobag_fun_brier(y_mat = pbc_orsf[,c('time', 'status')], s_vec = fit$pred_oobag) #> [1] 0.11724 fit <- orsf(data = pbc_orsf, formula = Surv(time, status) ~ . - id, n_tree = 50, oobag_pred_horizon = 2000, oobag_fun = oobag_fun_brier, oobag_eval_every = 1) plot( x = seq(1, 50, by = 1), y = fit$eval_oobag$stat_values, main = 'Out-of-bag error computed after each new tree is grown.', sub = 'For the Brier score, lower values indicate more accurate predictions', xlab = 'Number of trees grown', ylab = \"Brier score\" ) oobag_fun_tdep_cstat <- function(y_mat, w_vec, s_vec){ as.numeric( SurvMetrics::Cindex( object = Surv(time = y_mat[, 1], event = y_mat[, 2]), predicted = s_vec, t_star = 2000 ) ) } fit <- orsf(data = pbc_orsf, formula = Surv(time, status) ~ . - id, n_tree = 50, oobag_pred_horizon = 2000, oobag_fun = oobag_fun_tdep_cstat, oobag_eval_every = 1) plot( x = seq(50), y = fit$eval_oobag$stat_values, main = 'Out-of-bag time-dependent AUC\\ncomputed after each new tree is grown.', xlab = 'Number of trees grown', ylab = \"AUC at t = 2,000\" )"},{"path":"https://bcjaeger.github.io/aorsf/articles/oobag.html","id":"specific-instructions-on-user-supplied-functions","dir":"Articles","previous_headings":"User-supplied out-of-bag evaluation functions","what":"Specific instructions on user-supplied functions","title":"Out-of-bag predictions and evaluation","text":"User-supplied functions must: exactly three arguments named y_mat, w_vec, s_vec. return numeric output length 1 either conditions true, error occur. simple test make sure user-supplied function work aorsf package :","code":"# Helper code to make sure your oobag_fun function will work with aorsf # time and status values test_time <- seq(from = 1, to = 5, length.out = 100) test_status <- rep(c(0,1), each = 50) # y-matrix is presumed to contain time and status (with column names) y_mat <- cbind(time = test_time, status = test_status) # s_vec is presumed to be a vector of survival probabilities s_vec <- seq(0.9, 0.1, length.out = 100) # see 1 in the checklist above names(formals(oobag_fun_tdep_cstat)) == c(\"y_mat\", \"w_vec\", \"s_vec\") #> [1] TRUE TRUE TRUE test_output <- oobag_fun_tdep_cstat(y_mat = y_mat, w_vec = w_vec, s_vec = s_vec) # test output should be numeric is.numeric(test_output) #> [1] TRUE # test_output should be a numeric value of length 1 length(test_output) == 1 #> [1] TRUE"},{"path":"https://bcjaeger.github.io/aorsf/articles/oobag.html","id":"user-supplied-functions-for-negation-importance-","dir":"Articles","previous_headings":"","what":"User-supplied functions for negation importance.","title":"Out-of-bag predictions and evaluation","text":"Negation importance based --bag error, course may curious negation importance computed using different statistics. workflow exactly example , except two things: specify importance = 'negate' fit model. want use modified version C-stat, specifically 1 - C-stat, aorsf computes variable importance. Also, speed computations, going monitor --bag error .","code":"oobag_fun_tdep_cstat_inverse <- function(y_mat, w_vec, s_vec){ 1 - oobag_fun_tdep_cstat(y_mat, w_vec, s_vec) } fit_tdep_cstat <- orsf(data = pbc_orsf, formula = Surv(time, status) ~ . - id, n_tree = 100, oobag_pred_horizon = 2000, oobag_fun = oobag_fun_tdep_cstat_inverse, importance = 'negate') fit_tdep_cstat$importance #> bili copper sex protime age ascites #> 0.130946460 0.044500890 0.033850120 0.022515610 0.019551930 0.017677020 #> stage albumin chol spiders edema ast #> 0.017561950 0.016692050 0.011163150 0.007158130 0.007008088 0.006360200 #> trig hepato trt alk.phos platelet #> 0.005541530 0.004885160 0.002620090 0.001023750 -0.002403190"},{"path":"https://bcjaeger.github.io/aorsf/articles/oobag.html","id":"notes","dir":"Articles","previous_headings":"","what":"Notes","title":"Out-of-bag predictions and evaluation","text":"evaluating --bag error: oobag_pred_horizon input orsf() determines prediction horizon --bag predictions. prediction horizon needs specified evaluate prediction accuracy cases, examples . sure check case using functions, , sure oobag_pred_horizon matches prediction horizon used custom function. functions expect predicted risk (.e., 1 - predicted survival), others expect predicted survival. cases, also able use function whatsoever compute --bag prediction error estimating negation permutation importance, assuming passes tests . Unfortunately, exception riskRegression::Score(), one favorites. experimented riskRegression::Score found work try run C++. sure case.","code":""},{"path":"https://bcjaeger.github.io/aorsf/articles/pd.html","id":"partial-dependence-pd","dir":"Articles","previous_headings":"","what":"Partial dependence (PD)","title":"PD and ICE curves with ORSF","text":"Partial dependence (PD) shows expected prediction model function single predictor multiple predictors. expectation marginalized values predictors, giving something like multivariable adjusted estimate model’s prediction. Begin fitting ORSF ensemble. Set prediction horizon 5 years fit ensemble aorsf function pass ensemble assume want compute predictions 5 years.","code":"library(aorsf) pred_horizon <- 365.25 * 5 set.seed(329730) index_train <- sample(nrow(pbc_orsf), 150) pbc_orsf_train <- pbc_orsf[index_train, ] pbc_orsf_test <- pbc_orsf[-index_train, ] fit <- orsf(data = pbc_orsf_train, formula = Surv(time, status) ~ . - id, oobag_pred_horizon = pred_horizon) fit #> ---------- Oblique random survival forest #> #> Linear combinations: Accelerated #> N observations: 150 #> N events: 52 #> N trees: 500 #> N predictors total: 17 #> N predictors per node: 5 #> Average leaves per tree: 12 #> Min observations in leaf: 5 #> Min events in leaf: 1 #> OOB stat value: 0.83 #> OOB stat type: Harrell's C-statistic #> Variable importance: anova #> #> -----------------------------------------"},{"path":"https://bcjaeger.github.io/aorsf/articles/pd.html","id":"three-ways-to-compute-pd","dir":"Articles","previous_headings":"","what":"Three ways to compute PD","title":"PD and ICE curves with ORSF","text":"can compute PD three ways aorsf: using -bag predictions training data using --bag predictions training data using predictions new set data -bag PD indicates relationships model learned training. helpful goal interpret model. --bag PD indicates relationships model learned training using --bag data simulates application model new data. want test model’s reliability fairness new data don’t access large testing set. new data PD shows model predicts outcomes observations seen. helpful want test model’s reliability fairness. Let’s re-fit ORSF available data proceeding next sections.","code":"pd_inb <- orsf_pd_inb(fit, pred_spec = list(bili = 1:5)) pd_inb #> pred_horizon bili mean lwr medn upr #> 1: 1826.25 1 0.2156600 0.02011013 0.09831473 0.8008680 #> 2: 1826.25 2 0.2577688 0.03774419 0.15474649 0.8221929 #> 3: 1826.25 3 0.2999502 0.06374133 0.20647008 0.8435691 #> 4: 1826.25 4 0.3392310 0.08411776 0.25351577 0.8591273 #> 5: 1826.25 5 0.3697834 0.10610430 0.28239158 0.8696440 pd_oob <- orsf_pd_oob(fit, pred_spec = list(bili = 1:5)) pd_oob #> pred_horizon bili mean lwr medn upr #> 1: 1826.25 1 0.2151532 0.01827735 0.09723974 0.7980629 #> 2: 1826.25 2 0.2568631 0.03685066 0.14354244 0.8181867 #> 3: 1826.25 3 0.2986274 0.05900059 0.20896839 0.8335823 #> 4: 1826.25 4 0.3386074 0.07902099 0.24601781 0.8482977 #> 5: 1826.25 5 0.3696689 0.10423692 0.27977338 0.8523756 pd_test <- orsf_pd_new(fit, new_data = pbc_orsf_test, pred_spec = list(bili = 1:5)) pd_test #> pred_horizon bili mean lwr medn upr #> 1: 1826.25 1 0.2543878 0.02896385 0.1940600 0.8149734 #> 2: 1826.25 2 0.2954799 0.05000806 0.2471401 0.8323505 #> 3: 1826.25 3 0.3389602 0.07334663 0.3010648 0.8494444 #> 4: 1826.25 4 0.3800023 0.10459104 0.3519063 0.8597879 #> 5: 1826.25 5 0.4119512 0.12113773 0.3895548 0.8693355 set.seed(329730) fit <- orsf(pbc_orsf, Surv(time, status) ~ . -id, oobag_pred_horizon = pred_horizon)"},{"path":"https://bcjaeger.github.io/aorsf/articles/pd.html","id":"one-variable-one-horizon","dir":"Articles","previous_headings":"","what":"One variable, one horizon","title":"PD and ICE curves with ORSF","text":"Computing PD single variable straightforward: output shows expected predicted mortality risk men substantially higher women 5 years baseline.","code":"pd_sex <- orsf_pd_oob(fit, pred_spec = list(sex = c(\"m\", \"f\"))) pd_sex #> pred_horizon sex mean lwr medn upr #> 1: 1826.25 m 0.3556338 0.03685843 0.2388210 0.9403551 #> 2: 1826.25 f 0.3027138 0.01063007 0.1525368 0.9548015"},{"path":"https://bcjaeger.github.io/aorsf/articles/pd.html","id":"one-variable-moving-horizon","dir":"Articles","previous_headings":"","what":"One variable, moving horizon","title":"PD and ICE curves with ORSF","text":"effect predictor varies time? PD can show . inspection, can see males higher risk females difference risk grows time. can also seen viewing ratio expected risk time:","code":"pd_sex_tv <- orsf_pd_oob(fit, pred_spec = list(sex = c(\"m\", \"f\")), pred_horizon = seq(365, 365*5)) ggplot(pd_sex_tv, aes(x = pred_horizon, y = mean, color = sex)) + geom_line() + labs(x = 'Time since baseline', y = 'Expected risk') library(data.table) ratio_tv <- pd_sex_tv[ , .(ratio = mean[sex == 'm'] / mean[sex == 'f']), by = pred_horizon ] ggplot(ratio_tv, aes(x = pred_horizon, y = ratio)) + geom_line(color = 'grey') + geom_smooth(color = 'black', se = FALSE) + labs(x = 'time since baseline', y = 'ratio in expected risk for males versus females') #> `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = \"cs\")'"},{"path":"https://bcjaeger.github.io/aorsf/articles/pd.html","id":"multiple-variables-marginally","dir":"Articles","previous_headings":"","what":"Multiple variables, marginally","title":"PD and ICE curves with ORSF","text":"want compute PD marginally multiple variables, just list variable values pred_spec specify expand_grid = FALSE. Now tedious wanted variables? bet. ’s made function . bonus, printed output sorted least important variables. ’s easy enough turn ‘summary’ object data.table downstream plotting tables.","code":"pd_two_vars <- orsf_pd_oob(fit, pred_spec = list(sex = c(\"m\", \"f\"), bili = 1:5), expand_grid = FALSE) pd_two_vars #> pred_horizon variable value level mean lwr medn upr #> 1: 1826.25 sex NA m 0.3556338 0.03685843 0.2388210 0.9403551 #> 2: 1826.25 sex NA f 0.3027138 0.01063007 0.1525368 0.9548015 #> 3: 1826.25 bili 1 <NA> 0.2452129 0.01605591 0.1285905 0.8946964 #> 4: 1826.25 bili 2 <NA> 0.3013639 0.04076854 0.2001183 0.9148909 #> 5: 1826.25 bili 3 <NA> 0.3508351 0.05863497 0.2592947 0.9201450 #> 6: 1826.25 bili 4 <NA> 0.3941197 0.08353114 0.3193513 0.9270573 #> 7: 1826.25 bili 5 <NA> 0.4286023 0.10893179 0.3624517 0.9263783 pd_smry <- orsf_summarize_uni(fit) pd_smry #> #> -- bili (VI Rank: 1) ------------------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 0.80 0.2376199 0.1175227 0.05112242 0.3796784 #> 1.4 0.2639813 0.1491127 0.07001711 0.4313834 #> 3.5 0.3750645 0.2959060 0.16393912 0.5687616 #> #> -- copper (VI Rank: 2) ----------------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 43 0.2669529 0.1343897 0.05166615 0.4478705 #> 74 0.2910106 0.1560732 0.06745016 0.4930575 #> 129 0.3458559 0.2276424 0.10882113 0.5518172 #> #> -- sex (VI Rank: 3) -------------------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> m 0.3556338 0.2388210 0.10652523 0.5860674 #> f 0.3027138 0.1525368 0.05384831 0.5617088 #> #> -- stage (VI Rank: 4) ------------------------------------ #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 1 0.5826297 0.5285612 0.3996661 0.7538155 #> 2 0.5826297 0.5285612 0.3996661 0.7538155 #> 3 0.5826297 0.5285612 0.3996661 0.7538155 #> 4 0.5826297 0.5285612 0.3996661 0.7538155 #> #> -- age (VI Rank: 5) -------------------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 42 0.2759266 0.1460143 0.04501236 0.4636428 #> 50 0.3096357 0.1961013 0.05299745 0.5246981 #> 57 0.3454927 0.2378101 0.08210052 0.5872838 #> #> -- protime (VI Rank: 6) ---------------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 10 0.2863813 0.1552936 0.05388682 0.5048504 #> 11 0.3040031 0.1631759 0.05897970 0.5387056 #> 11 0.3268747 0.1901532 0.06986700 0.5837946 #> #> -- albumin (VI Rank: 7) ---------------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 3.3 0.3262871 0.1903782 0.06054024 0.6108546 #> 3.5 0.3036352 0.1556788 0.05654525 0.5458010 #> 3.8 0.2855665 0.1540199 0.05336999 0.5033495 #> #> -- ascites (VI Rank: 8) ---------------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 0 0.3020660 0.1499472 0.05384831 0.5426342 #> 1 0.4518423 0.3638425 0.24341274 0.6433297 #> #> -- ast (VI Rank: 9) -------------------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 82 0.2892887 0.1466898 0.05112062 0.5135115 #> 117 0.3067945 0.1587376 0.05443293 0.5522869 #> 153 0.3298465 0.1791157 0.07169660 0.6069204 #> #> -- chol (VI Rank: 10) ------------------------------------ #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 250 0.2927177 0.1496405 0.04763950 0.5048974 #> 310 0.3023735 0.1576780 0.05465480 0.5158055 #> 401 0.3248603 0.1923752 0.07233946 0.5526811 #> #> -- edema (VI Rank: 11) ----------------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 0 0.2959085 0.1499472 0.05285512 0.5406550 #> 0.5 0.3637250 0.2550075 0.10860243 0.6126169 #> 1 0.4575163 0.3709865 0.25421885 0.6489456 #> #> -- spiders (VI Rank: 12) --------------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 0 0.2976539 0.1499472 0.05137698 0.5310312 #> 1 0.3426720 0.2230450 0.09443604 0.5593377 #> #> -- hepato (VI Rank: 13) ---------------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 0 0.2912547 0.1493194 0.05056845 0.5242976 #> 1 0.3268190 0.1830504 0.07537275 0.5490061 #> #> -- trt (VI Rank: 14) ------------------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> d_penicill_main 0.3139442 0.1728924 0.06002565 0.5541061 #> placebo 0.3083033 0.1575168 0.05420006 0.5508226 #> #> -- trig (VI Rank: 15) ------------------------------------ #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 85 0.3011420 0.1531589 0.05085489 0.5339691 #> 108 0.3087066 0.1626084 0.05129435 0.5384522 #> 151 0.3229588 0.1833097 0.06437458 0.5452205 #> #> -- alk.phos (VI Rank: 16) -------------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 922 0.3123888 0.1719807 0.05639797 0.5655804 #> 1278 0.3140034 0.1688451 0.05799607 0.5687561 #> 2068 0.3170148 0.1684038 0.05899212 0.5944319 #> #> -- platelet (VI Rank: 17) -------------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 200 0.3169215 0.1771280 0.05563475 0.5902139 #> 257 0.3112003 0.1692829 0.05472123 0.5747745 #> 318 0.3078816 0.1724278 0.05521177 0.5625068 #> #> Predicted risk at time t = 1826.25 for top 17 predictors head(as.data.table(pd_smry)) #> variable importance Value Mean Median 25th % 75th % #> 1: bili 0.11347395 0.80 0.2376199 0.1175227 0.05112242 0.3796784 #> 2: bili 0.11347395 1.4 0.2639813 0.1491127 0.07001711 0.4313834 #> 3: bili 0.11347395 3.5 0.3750645 0.2959060 0.16393912 0.5687616 #> 4: copper 0.04899814 43 0.2669529 0.1343897 0.05166615 0.4478705 #> 5: copper 0.04899814 74 0.2910106 0.1560732 0.06745016 0.4930575 #> 6: copper 0.04899814 129 0.3458559 0.2276424 0.10882113 0.5518172 #> pred_horizon level #> 1: 1826.25 <NA> #> 2: 1826.25 <NA> #> 3: 1826.25 <NA> #> 4: 1826.25 <NA> #> 5: 1826.25 <NA> #> 6: 1826.25 <NA>"},{"path":"https://bcjaeger.github.io/aorsf/articles/pd.html","id":"multiple-variables-jointly","dir":"Articles","previous_headings":"","what":"Multiple variables, jointly","title":"PD and ICE curves with ORSF","text":"PD can show expected value model’s predictions function specific predictor, function multiple predictors. instance, can estimate predicted risk joint function bili, edema, trt: inspection, model’s predictions indicate slightly lower risk placebo group, seem change much different values bili edema. clear increase predicted risk higher levels edema higher levels bili slope predicted risk function bili appears highest among patients edema 0.5. effect bili modified edema 0.5? quick sanity check coxph suggests .","code":"pred_spec = list(bili = seq(1, 5, length.out = 20), edema = levels(pbc_orsf_train$edema), trt = levels(pbc_orsf$trt)) pd_bili_edema <- orsf_pd_oob(fit, pred_spec) library(ggplot2) ggplot(pd_bili_edema, aes(x = bili, y = medn, col = trt, linetype = edema)) + geom_line() + labs(y = 'Expected predicted risk') library(survival) pbc_orsf$edema_05 <- ifelse(pbc_orsf$edema == '0.5', 'yes', 'no') fit_cph <- coxph(Surv(time,status) ~ edema_05 * bili, data = pbc_orsf) anova(fit_cph) #> Analysis of Deviance Table #> Cox model: response is Surv(time, status) #> Terms added sequentially (first to last) #> #> loglik Chisq Df Pr(>|Chi|) #> NULL -550.19 #> edema_05 -546.83 6.7248 1 0.009508 ** #> bili -513.59 66.4689 1 3.555e-16 *** #> edema_05:bili -510.54 6.1112 1 0.013433 * #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1"},{"path":"https://bcjaeger.github.io/aorsf/articles/pd.html","id":"individual-conditional-expectations-ice","dir":"Articles","previous_headings":"","what":"Individual conditional expectations (ICE)","title":"PD and ICE curves with ORSF","text":"Unlike partial dependence, shows expected prediction function one multiple predictors, individual conditional expectations (ICE) show prediction individual observation function predictor. Just like PD, can compute ICE using -bag, --bag, testing data, principles apply. ’ll use --bag estimates .","code":""},{"path":"https://bcjaeger.github.io/aorsf/articles/pd.html","id":"visualizing-ice-curves","dir":"Articles","previous_headings":"","what":"Visualizing ICE curves","title":"PD and ICE curves with ORSF","text":"Inspecting ICE curves observation can help identify whether heterogeneity model’s predictions. .e., effect variable follow pattern data, groups variable impacts risk differently? going turn boundary checking orsf_ice_oob setting boundary_checks = FALSE, allow generate ICE curves go beyond 90th percentile bili. id_variable identifier current value variable(s) data. redundant one variable, helpful multiple variables. id_row identifier observation original data. used group observation’s predictions together plots. plots, helpful scale ICE data. subtract initial value predicted risk (.e., bili = 1) observation’s conditional expectation values. , Every curve start 0 plot shows change predicted risk function bili. Now can visualize curves. inspection figure, individual slopes cluster around overall trend - Good! small number individual slopes appear flat. may helpful investigate .","code":"pred_spec <- list(bili = seq(1, 10, length.out = 25)) ice_oob <- orsf_ice_oob(fit, pred_spec, boundary_checks = FALSE) ice_oob #> id_variable id_row pred_horizon bili pred #> 1: 1 1 1826.25 1 0.9216359 #> 2: 1 2 1826.25 1 0.1141265 #> 3: 1 3 1826.25 1 0.7337662 #> 4: 1 4 1826.25 1 0.3585570 #> 5: 1 5 1826.25 1 0.1417314 #> --- #> 6896: 25 272 1826.25 10 0.3264152 #> 6897: 25 273 1826.25 10 0.4338510 #> 6898: 25 274 1826.25 10 0.4856256 #> 6899: 25 275 1826.25 10 0.3136700 #> 6900: 25 276 1826.25 10 0.5347941 ice_oob[, pred_subtract := rep(pred[id_variable==1], times=25)] ice_oob[, pred := pred - pred_subtract] library(ggplot2) ggplot(ice_oob, aes(x = bili, y = pred, group = id_row)) + geom_line(alpha = 0.15) + labs(y = 'Change in predicted risk') + geom_smooth(se = FALSE, aes(group = 1)) #> `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = \"cs\")'"},{"path":"https://bcjaeger.github.io/aorsf/articles/pd.html","id":"limitations-of-pd","dir":"Articles","previous_headings":"","what":"Limitations of PD","title":"PD and ICE curves with ORSF","text":"Partial dependence number known limitations assumptions users aware (see Hooker, 2021). particular, partial dependence less intuitive >2 predictors examined jointly, assumed feature(s) partial dependence computed correlated features (likely true many cases). Accumulated local effect plots can used (see ) case feature independence valid assumption.","code":""},{"path":"https://bcjaeger.github.io/aorsf/articles/pd.html","id":"references","dir":"Articles","previous_headings":"","what":"References","title":"PD and ICE curves with ORSF","text":"Giles Hooker, Lucas Mentch, Siyu Zhou. Unrestricted Permutation forces Extrapolation: Variable Importance Requires least One Model, Free Variable Importance. arXiv e-prints 2021 Oct; arXiv-1905. URL: https://doi.org/10.48550/arXiv.1905.03151","code":""},{"path":"https://bcjaeger.github.io/aorsf/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Byron Jaeger. Author, maintainer. Nicholas Pajewski. Contributor. Sawyer Welden. Contributor. Christopher Jackson. Reviewer. Marvin Wright. Reviewer. Lukas Burk. Reviewer.","code":""},{"path":"https://bcjaeger.github.io/aorsf/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Jaeger et al. (2022). aorsf: R package supervised learning using oblique random survival forest. Journal Open Source Software, 7(77), 4705. https://doi.org/10.21105/joss.04705. Jaeger BC, Welden S, Lenoir K, Speiser JL, Segar MW, Pandey , Pajewski NM. Accelerated interpretable oblique random survival forests. arXiv e-prints. 2022 Aug 3:arXiv-2208. Jaeger BC, Long DL, Long DM, Sims M, Szychowski JM, Min YI, Mcclure LA, Howard G, Simon N. Oblique Random Survival Forests. Annals Applied Statistics. 13(3): 1847-1883. URL https://doi.org/10.1214/19-AOAS1261 DOI: 10.1214/19-AOAS1261","code":"@Article{, title = {aorsf: An R package for supervised learning using the oblique random survival forest}, author = {Byron C. Jaeger and Sawyer Welden and Kristin Lenoir and Nicholas M. Pajewski}, journal = {Journal of Open Source Software}, year = {2022}, volume = {7}, number = {77}, pages = {4705}, url = {https://doi.org/10.21105/joss.04705}, } @Article{, title = {Accelerated and interpretable oblique random survival forests}, author = {Byron C. Jaeger and Sawyer Welden and Kristin Lenoir and Jaime L. Speiser and Matthew W. Segar and Ambarish Pandey and Nicholas M. Pajewski}, journal = {arXiv}, year = {2022}, url = {https://arxiv.org/abs/2208.01129}, } @Article{, title = {Oblique Random Survival Forests}, author = {Byron C. Jaeger and D. Leann Long and Dustin M. Long and Mario Sims and Jeff M. Szychowski and Yuan-I Min and Leslie A. Mcclure and George Howard and Noah Simon}, journal = {Annals of Applied Statistics}, year = {2019}, volume = {13}, number = {3}, pages = {1847--1883}, url = {https://doi.org/10.1214/19-AOAS1261}, }"},{"path":"https://bcjaeger.github.io/aorsf/index.html","id":"aorsf-","dir":"","previous_headings":"","what":"Accelerated Oblique Random Survival Forests","title":"Accelerated Oblique Random Survival Forests","text":"Fit, interpret, make predictions oblique random survival forests (ORSFs).","code":""},{"path":"https://bcjaeger.github.io/aorsf/index.html","id":"why-aorsf","dir":"","previous_headings":"","what":"Why aorsf?","title":"Accelerated Oblique Random Survival Forests","text":"Hundreds times faster obliqueRSF.1 Accurate predictions censored outcomes.2 Negation importance, novel technique estimate variable importance ORSFs.2 Intuitive API formula based interface. Extensive input checks informative error messages.","code":""},{"path":"https://bcjaeger.github.io/aorsf/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Accelerated Oblique Random Survival Forests","text":"can install aorsf CRAN using can install development version aorsf GitHub :","code":"install.packages(\"aorsf\") # install.packages(\"remotes\") remotes::install_github(\"ropensci/aorsf\")"},{"path":"https://bcjaeger.github.io/aorsf/index.html","id":"what-is-an-oblique-decision-tree","dir":"","previous_headings":"","what":"What is an oblique decision tree?","title":"Accelerated Oblique Random Survival Forests","text":"Decision trees developed splitting set training data two new subsets, goal similarity within new subsets . splitting process repeated resulting subsets data stopping criterion met. new subsets data formed based single predictor, decision tree said axis-based splits data appear perpendicular axis predictor. linear combinations variables used instead single variable, tree oblique splits data neither parallel right angle axis. Figure: Decision trees classification axis-based splitting (left) oblique splitting (right). Cases orange squares; controls purple circles. trees partition predictor space defined variables X1 X2, oblique splits better job separating two classes.","code":""},{"path":"https://bcjaeger.github.io/aorsf/index.html","id":"examples","dir":"","previous_headings":"","what":"Examples","title":"Accelerated Oblique Random Survival Forests","text":"orsf() function can fit several types ORSF ensembles. personal favorite accelerated ORSF great combination prediction accuracy computational efficiency (see arXiv paper).2","code":"library(aorsf) set.seed(329730) index_train <- sample(nrow(pbc_orsf), 150) pbc_orsf_train <- pbc_orsf[index_train, ] pbc_orsf_test <- pbc_orsf[-index_train, ] fit <- orsf(data = pbc_orsf_train, formula = Surv(time, status) ~ . - id, oobag_pred_horizon = 365.25 * 5)"},{"path":"https://bcjaeger.github.io/aorsf/index.html","id":"inspect","dir":"","previous_headings":"Examples","what":"Inspect","title":"Accelerated Oblique Random Survival Forests","text":"Printing output orsf() give information descriptive statistics ensemble. See print.orsf_fit description line printed output. See orsf examples details controlling ORSF ensemble fits using prediction modeling workflows.","code":"fit #> ---------- Oblique random survival forest #> #> Linear combinations: Accelerated #> N observations: 150 #> N events: 52 #> N trees: 500 #> N predictors total: 17 #> N predictors per node: 5 #> Average leaves per tree: 12 #> Min observations in leaf: 5 #> Min events in leaf: 1 #> OOB stat value: 0.83 #> OOB stat type: Harrell's C-statistic #> Variable importance: anova #> #> -----------------------------------------"},{"path":"https://bcjaeger.github.io/aorsf/index.html","id":"variable-importance","dir":"","previous_headings":"Examples","what":"Variable importance","title":"Accelerated Oblique Random Survival Forests","text":"importance individual variables can estimated three ways using aorsf: negation2: variable assessed separately multiplying variable’s coefficients -1 determining much model’s performance changes. worse model’s performance negating coefficients given variable, important variable. technique promising b/c require permutation emphasizes variables larger coefficients linear combinations, also relatively new hasn’t studied much permutation importance. See Jaeger, 2022 details technique. permutation: variable assessed separately randomly permuting variable’s values determining much model’s performance changes. worse model’s performance permuting values given variable, important variable. technique flexible, intuitive, frequently used. also several known limitations analysis variance (ANOVA)3: p-value computed coefficient linear combination variables decision tree. Importance individual predictor variable proportion times p-value coefficient < 0.01. technique efficient computationally, may effective permutation negation terms selecting signal noise variables. See Menze, 2011 details technique. can supply R function estimate --bag error using negation permutation importance. feature experimental may changed future (see oob vignette)","code":"orsf_vi_negate(fit) #> bili age sex ast ascites #> 0.0959635932 0.0162247725 0.0136525524 0.0085081124 0.0059358924 #> edema stage copper hepato chol #> 0.0051286110 0.0019786308 0.0015829046 0.0007914523 -0.0003957262 #> alk.phos albumin spiders trt platelet #> -0.0021764939 -0.0023743569 -0.0043529877 -0.0045508508 -0.0059358924 orsf_vi_permute(fit) #> bili ascites sex age edema #> 0.0096952909 0.0073209339 0.0067273447 0.0065294816 0.0037989711 #> albumin stage protime hepato chol #> 0.0031658093 0.0029679462 0.0023743569 0.0019786308 0.0007914523 #> ast spiders copper trt trig #> 0.0003957262 -0.0019786308 -0.0027700831 -0.0049465770 -0.0055401662 orsf_vi_anova(fit) #> ascites bili edema sex age copper stage #> 0.35231788 0.33216374 0.31401592 0.22045995 0.19044776 0.18155620 0.16907605 #> ast hepato albumin chol trig protime spiders #> 0.14183124 0.13736655 0.12611012 0.11461988 0.10847044 0.10697115 0.08802817 #> alk.phos platelet trt #> 0.07943094 0.06150342 0.04411765"},{"path":"https://bcjaeger.github.io/aorsf/index.html","id":"partial-dependence-pd","dir":"","previous_headings":"Examples","what":"Partial dependence (PD)","title":"Accelerated Oblique Random Survival Forests","text":"Partial dependence (PD) shows expected prediction model function single predictor multiple predictors. expectation marginalized values predictors, giving something like multivariable adjusted estimate model’s prediction. PD, see vignette","code":""},{"path":"https://bcjaeger.github.io/aorsf/index.html","id":"individual-conditional-expectations-ice","dir":"","previous_headings":"Examples","what":"Individual conditional expectations (ICE)","title":"Accelerated Oblique Random Survival Forests","text":"Unlike partial dependence, shows expected prediction function one multiple predictors, individual conditional expectations (ICE) show prediction individual observation function predictor. ICE, see vignette","code":""},{"path":"https://bcjaeger.github.io/aorsf/index.html","id":"comparison-to-existing-software","dir":"","previous_headings":"","what":"Comparison to existing software","title":"Accelerated Oblique Random Survival Forests","text":"Comparisons aorsf existing software presented arXiv paper. paper describes aorsf detail summary procedures used tree fitting algorithm runs general benchmark comparing aorsf obliqueRSF several learners reports prediction accuracy computational efficiency learners. runs simulation study comparing variable importance techniques ORSFs, axis based RSFs, boosted trees. reports probability variable importance technique rank relevant variable higher importance irrelevant variable. hands-comparison aorsf R packages provided orsf examples","code":""},{"path":"https://bcjaeger.github.io/aorsf/index.html","id":"references","dir":"","previous_headings":"","what":"References","title":"Accelerated Oblique Random Survival Forests","text":"Jaeger BC, Long DL, Long DM, Sims M, Szychowski JM, Min YI, Mcclure LA, Howard G, Simon N. Oblique random survival forests. Annals applied statistics 2019 Sep; 13(3):1847-83. DOI: 10.1214/19-AOAS1261 Jaeger BC, Welden S, Lenoir K, Speiser JL, Segar MW, Pandey , Pajewski NM. Accelerated interpretable oblique random survival forests. arXiv e-prints 2022 Aug; arXiv-2208. URL: https://arxiv.org/abs/2208.01129 Menze BH, Kelm BM, Splitthoff DN, Koethe U, Hamprecht FA. oblique random forests. Joint European Conference Machine Learning Knowledge Discovery Databases 2011 Sep 4; pp. 453-469. DOI: 10.1007/978-3-642-23783-6_29","code":""},{"path":"https://bcjaeger.github.io/aorsf/index.html","id":"funding","dir":"","previous_headings":"","what":"Funding","title":"Accelerated Oblique Random Survival Forests","text":"developers aorsf receive financial support Center Biomedical Informatics, Wake Forest University School Medicine. also receive support National Center Advancing Translational Sciences National Institutes Health Award Number UL1TR001420. content solely responsibility authors necessarily represent official views National Institutes Health.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/aorsf-package.html","id":null,"dir":"Reference","previous_headings":"","what":"aorsf: Accelerated Oblique Random Survival Forests — aorsf-package","title":"aorsf: Accelerated Oblique Random Survival Forests — aorsf-package","text":"Fit, interpret, make predictions oblique random survival forests. Oblique decision trees notoriously slow compared axis based counterparts, 'aorsf' runs fast faster axis-based decision tree algorithms right-censored time--event outcomes. Methods accelerate interpret oblique random survival forest described Jaeger et al., (2022) arXiv:2208.01129.","code":""},{"path":[]},{"path":"https://bcjaeger.github.io/aorsf/reference/aorsf-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"aorsf: Accelerated Oblique Random Survival Forests — aorsf-package","text":"Maintainer: Byron Jaeger bjaeger@wakehealth.edu (ORCID) contributors: Nicholas Pajewski [contributor] Sawyer Welden swelden@wakehealth.edu [contributor] Christopher Jackson chris.jackson@mrc-bsu.cam.ac.uk [reviewer] Marvin Wright [reviewer] Lukas Burk [reviewer]","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/as.data.table.orsf_summary_uni.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce to data.table — as.data.table.orsf_summary_uni","title":"Coerce to data.table — as.data.table.orsf_summary_uni","text":"Convert 'orsf_summary' object data.table object.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/as.data.table.orsf_summary_uni.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce to data.table — as.data.table.orsf_summary_uni","text":"","code":"# S3 method for orsf_summary_uni as.data.table(x, ...)"},{"path":"https://bcjaeger.github.io/aorsf/reference/as.data.table.orsf_summary_uni.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce to data.table — as.data.table.orsf_summary_uni","text":"x object class 'orsf_summary_uni' ... used","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/as.data.table.orsf_summary_uni.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce to data.table — as.data.table.orsf_summary_uni","text":"data.table","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/as.data.table.orsf_summary_uni.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce to data.table — as.data.table.orsf_summary_uni","text":"","code":"library(data.table) object <- orsf(pbc_orsf, Surv(time, status) ~ . - id) smry <- orsf_summarize_uni(object, n_variables = 3) as.data.table(smry) #> variable importance value mean medn lwr upr #> 1: bili 0.12308231 0.80 0.2367744 0.1212470 0.05279761 0.3629509 #> 2: bili 0.12308231 1.4 0.2637149 0.1453019 0.07504962 0.4062557 #> 3: bili 0.12308231 3.5 0.3806357 0.2922601 0.17581820 0.5671431 #> 4: copper 0.04831185 43 0.2727479 0.1435153 0.05340342 0.4887602 #> 5: copper 0.04831185 74 0.2962180 0.1684203 0.06767541 0.5202639 #> 6: copper 0.04831185 129 0.3481921 0.2290269 0.11586820 0.5679235 #> 7: stage 0.03206122 1 0.5778359 0.5196442 0.39788860 0.7723695 #> 8: stage 0.03206122 2 0.5778359 0.5196442 0.39788860 0.7723695 #> 9: stage 0.03206122 3 0.5778359 0.5196442 0.39788860 0.7723695 #> 10: stage 0.03206122 4 0.5778359 0.5196442 0.39788860 0.7723695 #> pred_horizon level #> 1: 1788 <NA> #> 2: 1788 <NA> #> 3: 1788 <NA> #> 4: 1788 <NA> #> 5: 1788 <NA> #> 6: 1788 <NA> #> 7: 1788 1 #> 8: 1788 2 #> 9: 1788 3 #> 10: 1788 4"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":null,"dir":"Reference","previous_headings":"","what":"Oblique Random Survival Forest (ORSF) — orsf","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"Fit oblique random survival forest","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"","code":"orsf( data, formula, control = orsf_control_fast(), weights = NULL, n_tree = 500, n_split = 5, n_retry = 3, n_thread = 1, mtry = NULL, sample_with_replacement = TRUE, sample_fraction = 0.632, leaf_min_events = 1, leaf_min_obs = 5, split_rule = \"logrank\", split_min_events = 5, split_min_obs = 10, split_min_stat = switch(split_rule, logrank = 3.841459, cstat = 0.5), oobag_pred_type = \"surv\", oobag_pred_horizon = NULL, oobag_eval_every = n_tree, oobag_fun = NULL, importance = \"anova\", group_factors = TRUE, tree_seeds = NULL, attach_data = TRUE, no_fit = FALSE, na_action = \"fail\", verbose_progress = FALSE, ... ) orsf_train(object)"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"data data.frame, tibble, data.table contains relevant variables. formula (formula) response left hand side include time variable, followed status variable, may written inside call Surv (see examples). terms right names predictor variables. control (orsf_control) object returned one orsf_control functions: orsf_control_fast (default) uses single iteration Newton Raphson scoring identify linear combination predictors. orsf_control_cph uses Newton Raphson scoring convergence criteria met. orsf_control_net uses glmnet identify linear combinations predictors, similar Jaeger (2019). orsf_control_custom allows user apply function create linear combinations predictors. weights (numeric vector) Optional. given, input length equal nrow(data). Values weights treated like replication weights, .e., value 2 thing 2 observations data, containing copy corresponding person's data. Use weights cautiously, orsf count number observations events prior growing node tree, higher values weights lead deeper trees. n_tree (integer) number trees grow. Default n_tree = 500. n_split (integer) number cut-points assessed splitting node decision trees. Default n_split = 5. n_retry (integer) node can split, current linear combination inputs unable provide valid split, orsf try new linear combination based different set randomly selected predictors, n_retry times. Default n_retry = 3. Set n_retry = 0 prevent retries. n_thread (integer) number threads use growing trees, computing predictions, computing importance. Default one thread. use maximum number threads system provides concurrent execution, set n_thread = 0. mtry (integer) Number predictors randomly included candidates splitting node. default smallest integer greater square root number total predictors, .e., mtry = ceiling(sqrt(number predictors)) sample_with_replacement (logical) TRUE (default), observations sampled replacement -bag sample created decision tree. FALSE, observations sampled without replacement tree -bag sample containing sample_fraction% original sample. sample_fraction (double) proportion observations trees' -bag sample contain, relative number rows data. used sample_with_replacement FALSE. Default value 0.632. leaf_min_events (integer) minimum number events leaf node. Default leaf_min_events = 1 leaf_min_obs (integer) minimum number observations leaf node. Default leaf_min_obs = 5. split_rule (character) assess quality potential splitting rule node. Valid options 'logrank' : log-rank test statistic. 'cstat' : Harrell's concordance statistic. split_min_events (integer) minimum number events required node consider splitting . Default split_min_events = 5 split_min_obs (integer) minimum number observations required node consider splitting . Default split_min_obs = 10. split_min_stat (double) minimum test statistic required split node. Default 3.841459 split_rule = 'logrank' 0.50 split_rule = 'cstat'. splits found statistic exceeding split_min_stat, given node either becomes leaf retry occurs (n_retry retries). oobag_pred_type (character) type --bag predictions compute fitting ensemble. Valid options 'none' : compute --bag predictions 'risk' : probability event occurring oobag_pred_horizon. 'surv' : 1 - risk. 'chf' : cumulative hazard function oobag_pred_horizon. 'mort' : mortality, .e., number events expected observations training data identical given observation. oobag_pred_horizon (numeric) numeric value indicating time used --bag predictions. Default median observed times, .e., oobag_pred_horizon = median(time). oobag_eval_every (integer) --bag performance ensemble checked every oobag_eval_every trees. , oobag_eval_every = 10, --bag performance checked growing 10th tree, 20th tree, . Default oobag_eval_every = n_tree. oobag_fun (function) used evaluating --bag prediction accuracy every oobag_eval_every trees. oobag_fun = NULL (default), Harrell's C-statistic (1982) used evaluate accuracy. use oobag_fun note following: oobag_fun two inputs: y_mat s_vec y_mat two column matrix first column named 'time', second named 'status' s_vec numeric vector containing predicted survival probabilities. oobag_fun return numeric output length 1 details, see --bag vignette. importance (character) Indicate method variable importance: 'none': variable importance computed. 'anova': compute analysis variance (ANOVA) importance 'negate': compute negation importance 'permute': compute permutation importance details methods, see orsf_vi. group_factors (logical) relevant variable importance estimated. TRUE, importance factor variables reported overall aggregating importance individual levels factor. FALSE, importance individual factor levels returned. tree_seeds (integer vector) Optional. specified, random seeds set using values tree_seeds[] growing tree . Two forests grown number trees seeds exact --bag samples, making --bag error estimates forests comparable. NULL (default), seeds set training process. attach_data (logical) TRUE, copy training data attached output. helpful plan using functions like orsf_pd_oob orsf_summarize_uni interpret forest using training data. Default TRUE. no_fit (logical) TRUE, model fitting steps defined saved, training initiated. object returned can directly submitted orsf_train() long attach_data TRUE. na_action (character) happen data contains missing values (.e., NA values). Valid options : 'fail' : error thrown data contains NA values 'omit' : rows data incomplete data dropped 'impute_meanmode' : missing values continuous categorical variables data imputed using mean mode, respectively. Note option selected attach_data TRUE, data attached output imputed version data. verbose_progress (logical) TRUE, progress messages printed console. FALSE (default), nothing printed. ... arguments passed methods (currently used). object untrained 'aorsf' object, created setting no_fit = TRUE orsf().","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"accelerated oblique RSF object (aorsf)","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"function based similar ORSF function obliqueRSF R package. primary difference function runs much faster. speed increase attributable better management memory (.e., unnecessary copies inputs) using Newton Raphson scoring algorithm identify linear combinations inputs rather performing penalized regression using routines glmnet.modified Newton Raphson scoring algorithm function applies adaptation C++ routine developed Terry M. Therneau fits Cox proportional hazards models (see survival::coxph() specifically survival::coxph.fit()).","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"details-on-inputs","dir":"Reference","previous_headings":"","what":"Details on inputs","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"formula: response formula can survival object returned Surv function, can also just time status variables. .e., Surv(time, status) ~ . works just like time + status ~ . . symbol right hand side short-hand using variables data (omitting left hand side formula) predictors. order variables left hand side matters. .e., writing status + time ~ . make orsf assume status variable actually time variable. response variable can survival object stored data. example, y ~ . valid formula data$y inherits Surv class. Although can fit oblique random survival forest 1 predictor variable, formula least 2 predictors. reason recommendation linear combination predictors trivial one predictor. mtry: mtry parameter may temporarily reduced ensure least 2 events per predictor variable. occurs using orsf_control_cph coefficients Newton Raphson scoring algorithm may become unstable number covariates greater equal number events. reduction occur using orsf_control_net. oobag_fun: oobag_fun specified, used compute negation importance permutation importance, role ANOVA importance. n_thread: R function must called C++ (.e., user-supplied function compute --bag error identify linear combinations variables), n_thread automatically set 1 attempting run R functions multiple threads cause R session crash.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"what-is-an-oblique-decision-tree-","dir":"Reference","previous_headings":"","what":"What is an oblique decision tree?","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"Decision trees developed splitting set training data two new subsets, goal similarity within new subsets . splitting process repeated resulting subsets data stopping criterion met. new subsets data formed based single predictor, decision tree said axis-based splits data appear perpendicular axis predictor. linear combinations variables used instead single variable, tree oblique splits data neither parallel right angle axis Figure : Decision trees classification axis-based splitting (left) oblique splitting (right). Cases orange squares; controls purple circles. trees partition predictor space defined variables X1 X2, oblique splits better job separating two classes.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"what-is-a-random-forest-","dir":"Reference","previous_headings":"","what":"What is a random forest?","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"Random forests collections de-correlated decision trees. Predictions tree aggregated make ensemble prediction forest. details, see Breiman el, 2001.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"training-out-of-bag-error-and-testing","dir":"Reference","previous_headings":"","what":"Training, out-of-bag error, and testing","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"random forests, tree grown bootstrapped version training set. bootstrap samples selected replacement, bootstrapped training set contains two-thirds instances original training set. '--bag' data instances bootstrapped training set. tree random forest can make predictions --bag data, --bag predictions can aggregated make ensemble --bag prediction. Since --bag data used grow tree, accuracy ensemble --bag predictions approximate generalization error random forest. Generalization error refers error random forest's predictions applied predict outcomes data used train , .e., testing data.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"missing-data","dir":"Reference","previous_headings":"","what":"Missing data","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"Data passed aorsf functions allowed missing values. user impute missing values using R package purpose, recipes mlr3pipelines.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"First load relevant packages entry-point aorsf standard call orsf(): printing fit provides quick descriptive summaries:","code":"set.seed(329730) suppressPackageStartupMessages({ library(aorsf) library(survival) library(tidymodels) library(tidyverse) library(randomForestSRC) library(ranger) library(riskRegression) library(obliqueRSF) }) fit <- orsf(pbc_orsf, Surv(time, status) ~ . - id) fit ## ---------- Oblique random survival forest ## ## Linear combinations: Accelerated ## N observations: 276 ## N events: 111 ## N trees: 500 ## N predictors total: 17 ## N predictors per node: 5 ## Average leaves per tree: 25 ## Min observations in leaf: 5 ## Min events in leaf: 1 ## OOB stat value: 0.84 ## OOB stat type: Harrell's C-statistic ## Variable importance: anova ## ## -----------------------------------------"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"model-control","dir":"Reference","previous_headings":"","what":"Model control","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"examples make use orsf_control_ functions build compare models based --bag predictions. also standardize --bag samples using input argument tree_seeds","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"accelerated-linear-combinations","dir":"Reference","previous_headings":"","what":"Accelerated linear combinations","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"accelerated ORSF ensemble default nice balance computational speed prediction accuracy. runs single iteration Newton Raphson scoring Cox partial likelihood function find linear combinations predictors.","code":"fit_accel <- orsf(pbc_orsf, control = orsf_control_fast(), formula = Surv(time, status) ~ . - id, tree_seeds = 1:500)"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"linear-combinations-with-cox-regression","dir":"Reference","previous_headings":"","what":"Linear combinations with Cox regression","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"orsf_control_cph runs Cox regression non-terminal node survival tree, using regression coefficients create linear combinations predictors:","code":"fit_cph <- orsf(pbc_orsf, control = orsf_control_cph(), formula = Surv(time, status) ~ . - id, tree_seeds = 1:500)"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"linear-combinations-with-penalized-cox-regression","dir":"Reference","previous_headings":"","what":"Linear combinations with penalized cox regression","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"orsf_control_net runs penalized Cox regression non-terminal node survival tree, using regression coefficients create linear combinations predictors. can really helpful want feature selection within node, lot slower options.","code":"fit_net <- orsf(pbc_orsf, # select 3 predictors out of 5 to be used in # each linear combination of predictors. control = orsf_control_net(df_target = 3), formula = Surv(time, status) ~ . - id, tree_seeds = 1:500)"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"linear-combinations-with-your-own-function","dir":"Reference","previous_headings":"","what":"Linear combinations with your own function","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"Let’s make two customized functions identify linear combinations predictors. first uses random coefficients second derives coefficients principal component analysis. can plug functions orsf_control_custom(), pass result orsf(): fit seems work best example? Let’s find evaluating --bag survival predictions. AUC values, highest lowest: indices prediction accuracy: inspection, glmnet approach highest discrimination index prediction accuracy. accelerated ORSF close second. random coefficients don’t well, aren’t bad.","code":"f_rando <- function(x_node, y_node, w_node){ matrix(runif(ncol(x_node)), ncol=1) } f_pca <- function(x_node, y_node, w_node) { # estimate two principal components. pca <- stats::prcomp(x_node, rank. = 2) # use the second principal component to split the node pca$rotation[, 1L, drop = FALSE] } fit_rando <- orsf(pbc_orsf, Surv(time, status) ~ . - id, control = orsf_control_custom(beta_fun = f_rando), tree_seeds = 1:500) fit_pca <- orsf(pbc_orsf, Surv(time, status) ~ . - id, control = orsf_control_custom(beta_fun = f_pca), tree_seeds = 1:500) risk_preds <- list( accel = 1 - fit_accel$pred_oobag, cph = 1 - fit_cph$pred_oobag, net = 1 - fit_net$pred_oobag, rando = 1 - fit_rando$pred_oobag, pca = 1 - fit_pca$pred_oobag ) sc <- Score(object = risk_preds, formula = Surv(time, status) ~ 1, data = pbc_orsf, summary = 'IPA', times = fit_accel$pred_horizon) sc$AUC$score[order(-AUC)] ## model times AUC se lower upper ## 1: net 1788 0.9179396 0.02012887 0.8784877 0.9573915 ## 2: accel 1788 0.9106396 0.02076004 0.8699507 0.9513286 ## 3: cph 1788 0.9061167 0.02277540 0.8614777 0.9507556 ## 4: rando 1788 0.8997729 0.02201363 0.8566270 0.9429188 ## 5: pca 1788 0.8996927 0.02245483 0.8556821 0.9437034 sc$Brier$score[order(-IPA), .(model, times, IPA)] ## model times IPA ## 1: net 1788 0.5020652 ## 2: cph 1788 0.4759061 ## 3: accel 1788 0.4743392 ## 4: pca 1788 0.4398468 ## 5: rando 1788 0.4219209 ## 6: Null model 1788 0.0000000"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"tidymodels","dir":"Reference","previous_headings":"","what":"tidymodels","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"example uses tidymodels functions stops short using official tidymodels workflow. working getting aorsf pulled censored package update real workflows happens!","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"comparing-orsf-with-other-learners","dir":"Reference","previous_headings":"","what":"Comparing ORSF with other learners","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"Start recipe pre-process data Next create 10-fold cross validation object pre-process data: Define functions ‘workflow’ randomForestSRC, ranger, aorsf. Run ‘workflows’ fold: Next unnest column get back tibble testing data predictions. finish aggregating predictions computing performance testing data. Note computing one statistic predictions instead computing one statistic fold. approach fine smaller testing sets /small event counts. inspection, aorsf obtained slightly higher discrimination (AUC) aorsf obtained higher index prediction accuracy (IPA)","code":"imputer <- recipe(pbc_orsf, formula = time + status ~ .) %>% step_impute_mean(all_numeric_predictors()) %>% step_impute_mode(all_nominal_predictors()) # 10-fold cross validation; make a container for the pre-processed data analyses <- vfold_cv(data = pbc_orsf, v = 10) %>% mutate(recipe = map(splits, ~prep(imputer, training = training(.x))), train = map(recipe, juice), test = map2(splits, recipe, ~bake(.y, new_data = testing(.x)))) analyses ## # 10-fold cross-validation ## # A tibble: 10 x 5 ## splits id recipe train test ## <list> <chr> <list> <list> <list> ## 1 <split [248/28]> Fold01 <recipe> <tibble [248 x 20]> <tibble [28 x 20]> ## 2 <split [248/28]> Fold02 <recipe> <tibble [248 x 20]> <tibble [28 x 20]> ## 3 <split [248/28]> Fold03 <recipe> <tibble [248 x 20]> <tibble [28 x 20]> ## 4 <split [248/28]> Fold04 <recipe> <tibble [248 x 20]> <tibble [28 x 20]> ## 5 <split [248/28]> Fold05 <recipe> <tibble [248 x 20]> <tibble [28 x 20]> ## 6 <split [248/28]> Fold06 <recipe> <tibble [248 x 20]> <tibble [28 x 20]> ## 7 <split [249/27]> Fold07 <recipe> <tibble [249 x 20]> <tibble [27 x 20]> ## 8 <split [249/27]> Fold08 <recipe> <tibble [249 x 20]> <tibble [27 x 20]> ## 9 <split [249/27]> Fold09 <recipe> <tibble [249 x 20]> <tibble [27 x 20]> ## 10 <split [249/27]> Fold10 <recipe> <tibble [249 x 20]> <tibble [27 x 20]> rfsrc_wf <- function(train, test, pred_horizon){ # rfsrc does not like tibbles, so cast input data into data.frames train <- as.data.frame(train) test <- as.data.frame(test) rfsrc(formula = Surv(time, status) ~ ., data = train) %>% predictRisk(newdata = test, times = pred_horizon) %>% as.numeric() } ranger_wf <- function(train, test, pred_horizon){ ranger(Surv(time, status) ~ ., data = train) %>% predictRisk(newdata = test, times = pred_horizon) %>% as.numeric() } aorsf_wf <- function(train, test, pred_horizon){ train %>% orsf(Surv(time, status) ~ .,) %>% predict(new_data = test, pred_horizon = pred_horizon) %>% as.numeric() } # 5 year risk prediction ph <- 365.25 * 5 results <- analyses %>% transmute(test, pred_aorsf = map2(train, test, aorsf_wf, pred_horizon = ph), pred_rfsrc = map2(train, test, rfsrc_wf, pred_horizon = ph), pred_ranger = map2(train, test, ranger_wf, pred_horizon = ph)) results <- results %>% unnest(everything()) glimpse(results) ## Rows: 276 ## Columns: 23 ## $ id <int> 8, 13, 31, 33, 35, 38, 83, 120, 127, 133, 143, 163, 165, 1~ ## $ trt <fct> placebo, placebo, placebo, placebo, placebo, placebo, d_pe~ ## $ age <dbl> 53.05681, 45.68925, 41.55236, 51.28268, 48.61875, 36.62697~ ## $ sex <fct> f, f, f, f, f, f, f, m, f, m, f, f, m, f, f, f, f, f, f, f~ ## $ ascites <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~ ## $ hepato <fct> 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1~ ## $ spiders <fct> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1~ ## $ edema <fct> 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,~ ## $ bili <dbl> 0.3, 0.7, 4.7, 0.8, 1.2, 3.3, 1.3, 3.5, 0.5, 1.5, 2.9, 0.3~ ## $ chol <int> 280, 281, 296, 210, 314, 383, 250, 325, 268, 331, 332, 233~ ## $ albumin <dbl> 4.00, 3.85, 3.44, 3.19, 3.20, 3.53, 3.50, 3.98, 4.08, 3.95~ ## $ copper <int> 52, 40, 114, 82, 201, 102, 48, 444, 9, 13, 86, 20, 80, 67,~ ## $ alk.phos <dbl> 4651.2, 1181.0, 9933.2, 1592.0, 12258.8, 1234.0, 1138.0, 7~ ## $ ast <dbl> 28.38, 88.35, 206.40, 218.55, 72.24, 137.95, 71.30, 130.20~ ## $ trig <int> 189, 130, 101, 113, 151, 87, 100, 210, 95, 99, 103, 68, 14~ ## $ platelet <int> 373, 244, 195, 180, 431, 234, 81, 344, 453, 165, 277, 358,~ ## $ protime <dbl> 11.0, 10.6, 10.3, 12.0, 10.6, 11.0, 12.9, 10.6, 10.0, 10.1~ ## $ stage <ord> 3, 3, 2, 3, 3, 4, 4, 3, 2, 4, 4, 3, 4, 3, 2, 3, 4, 3, 3, 3~ ## $ time <int> 2466, 3577, 3839, 3170, 2847, 3244, 4050, 2033, 3255, 2796~ ## $ status <dbl> 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0~ ## $ pred_aorsf <dbl> 0.06002419, 0.01954988, 0.35024244, 0.29486541, 0.23418878~ ## $ pred_rfsrc <dbl> 0.052628661, 0.010204564, 0.401535927, 0.259857534, 0.3263~ ## $ pred_ranger <dbl> 0.040042884, 0.012915865, 0.392153766, 0.347688672, 0.3015~ Score( object = list(aorsf = results$pred_aorsf, rfsrc = results$pred_rfsrc, ranger = results$pred_ranger), formula = Surv(time, status) ~ 1, data = results, summary = 'IPA', times = ph ) ## ## Metric AUC: ## ## Results by model: ## ## model times AUC lower upper ## 1: aorsf 1826 91.3 87.2 95.5 ## 2: rfsrc 1826 90.0 85.8 94.3 ## 3: ranger 1826 90.6 86.6 94.7 ## ## Results of model comparisons: ## ## times model reference delta.AUC lower upper p ## 1: 1826 rfsrc aorsf -1.3 -2.9 0.3 0.1 ## 2: 1826 ranger aorsf -0.7 -2.3 0.9 0.4 ## 3: 1826 ranger rfsrc 0.6 -0.5 1.7 0.3 ## ## NOTE: Values are multiplied by 100 and given in %. ## NOTE: The higher AUC the better. ## ## Metric Brier: ## ## Results by model: ## ## model times Brier lower upper IPA ## 1: Null model 1826.25 20.5 18.1 22.9 0.0 ## 2: aorsf 1826.25 10.6 8.5 12.8 48.0 ## 3: rfsrc 1826.25 11.8 9.7 13.9 42.4 ## 4: ranger 1826.25 11.5 9.5 13.5 44.0 ## ## Results of model comparisons: ## ## times model reference delta.Brier lower upper p ## 1: 1826.25 aorsf Null model -9.8 -12.5 -7.2 1.872048e-13 ## 2: 1826.25 rfsrc Null model -8.7 -10.9 -6.5 2.176184e-14 ## 3: 1826.25 ranger Null model -9.0 -11.3 -6.7 1.387967e-14 ## 4: 1826.25 rfsrc aorsf 1.1 0.3 2.0 8.934160e-03 ## 5: 1826.25 ranger aorsf 0.8 0.1 1.6 3.287486e-02 ## 6: 1826.25 ranger rfsrc -0.3 -0.9 0.2 2.459287e-01 ## ## NOTE: Values are multiplied by 100 and given in %. ## NOTE: The lower Brier the better, the higher IPA the better."},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"mlr-pipelines","dir":"Reference","previous_headings":"","what":"mlr3 pipelines","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"Warning: code may may run depending current version mlr3proba. First load additional mlr3 libraries. Next ’ll define tasks learners engage . Now can make benchmark designed compare three favorite learners: Let’s look overall results: inspection, aorsf higher expected value ‘surv.cindex’ (higher better) aorsf lower expected value ‘surv.graf’ (lower better)","code":"suppressPackageStartupMessages({ library(mlr3verse) library(mlr3proba) library(mlr3extralearners) library(mlr3viz) library(mlr3benchmark) }) # Mayo Clinic Primary Biliary Cholangitis Data task_pbc <- TaskSurv$new( id = 'pbc', backend = select(pbc_orsf, -id) %>% mutate(stage = as.numeric(stage)), time = \"time\", event = \"status\" ) # Veteran's Administration Lung Cancer Trial data(veteran, package = \"randomForestSRC\") task_veteran <- TaskSurv$new( id = 'veteran', backend = veteran, time = \"time\", event = \"status\" ) # NKI 70 gene signature data_nki <- OpenML::getOMLDataSet(data.id = 1228) task_nki <- TaskSurv$new( id = 'nki', backend = data_nki$data, time = \"time\", event = \"event\" ) # Gene Expression-Based Survival Prediction in Lung Adenocarcinoma data_lung <- OpenML::getOMLDataSet(data.id = 1245) task_lung <- TaskSurv$new( id = 'nki', backend = data_lung$data %>% mutate(OS_event = as.numeric(OS_event) -1), time = \"OS_years\", event = \"OS_event\" ) # Chemotherapy for Stage B/C colon cancer # (there are two rows per person, one for death # and the other for recurrence, hence the two tasks) task_colon_death <- TaskSurv$new( id = 'colon_death', backend = survival::colon %>% filter(etype == 2) %>% drop_na() %>% # drop id, redundant variables select(-id, -study, -node4, -etype), mutate(OS_event = as.numeric(OS_event) -1), time = \"time\", event = \"status\" ) task_colon_recur <- TaskSurv$new( id = 'colon_death', backend = survival::colon %>% filter(etype == 1) %>% drop_na() %>% # drop id, redundant variables select(-id, -study, -node4, -etype), mutate(OS_event = as.numeric(OS_event) -1), time = \"time\", event = \"status\" ) # putting them all together tasks <- list(task_pbc, task_veteran, task_nki, task_lung, task_colon_death, task_colon_recur, # add a few more pre-made ones tsk(\"actg\"), tsk('gbcs'), tsk('grace'), tsk(\"unemployment\"), tsk(\"whas\")) # Learners with default parameters learners <- lrns(c(\"surv.ranger\", \"surv.rfsrc\", \"surv.aorsf\")) # Brier (Graf) score, c-index and training time as measures measures <- msrs(c(\"surv.graf\", \"surv.cindex\", \"time_train\")) # Benchmark with 5-fold CV design <- benchmark_grid( tasks = tasks, learners = learners, resamplings = rsmps(\"cv\", folds = 5) ) benchmark_result <- benchmark(design) bm_scores <- benchmark_result$score(measures, predict_sets = \"test\") bm_scores %>% select(task_id, learner_id, surv.graf, surv.cindex, time_train) %>% group_by(learner_id) %>% filter(!is.infinite(surv.graf)) %>% summarize( across( .cols = c(surv.graf, surv.cindex, time_train), .fns = mean, na.rm = TRUE ) ) ## # A tibble: 3 x 4 ## learner_id surv.graf surv.cindex time_train ## <chr> <dbl> <dbl> <dbl> ## 1 surv.aorsf 0.152 0.733 1.41 ## 2 surv.ranger 0.166 0.712 1.95 ## 3 surv.rfsrc 0.155 0.723 0.745"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"Harrell FE, Califf RM, Pryor DB, Lee KL, Rosati RA. Evaluating Yield Medical Tests. JAMA 1982; 247(18):2543-2546. DOI: 10.1001/jama.1982.03320430047030 Breiman L. Random forests. Machine learning 2001 Oct; 45(1):5-32. DOI: 10.1023/:1010933404324 Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS. Random survival forests. Annals applied statistics 2008 Sep; 2(3):841-60. DOI: 10.1214/08-AOAS169 Jaeger BC, Long DL, Long DM, Sims M, Szychowski JM, Min YI, Mcclure LA, Howard G, Simon N. Oblique random survival forests. Annals applied statistics 2019 Sep; 13(3):1847-83. DOI: 10.1214/19-AOAS1261 Jaeger BC, Welden S, Lenoir K, Speiser JL, Segar MW, Pandey , Pajewski NM. Accelerated interpretable oblique random survival forests. arXiv e-prints 2022 Aug; arXiv-2208. URL: https://arxiv.org/abs/2208.01129","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_cph.html","id":null,"dir":"Reference","previous_headings":"","what":"Cox regression ORSF control — orsf_control_cph","title":"Cox regression ORSF control — orsf_control_cph","text":"Use coefficients proportional hazards model create linear combinations predictor variables fitting orsf model.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_cph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cox regression ORSF control — orsf_control_cph","text":"","code":"orsf_control_cph(method = \"efron\", eps = 1e-09, iter_max = 20, ...)"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_cph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cox regression ORSF control — orsf_control_cph","text":"method (character) character string specifying method tie handling. ties, methods equivalent. Valid options 'breslow' 'efron'. Efron approximation default accurate dealing tied event times similar computational efficiency compared Breslow method. eps (double) using Newton Raphson scoring identify linear combinations inputs, iteration continues algorithm relative change log partial likelihood less eps, absolute change less sqrt(eps). Must positive. default value 1e-09 used consistency survival::coxph.control. iter_max (integer) iteration continues convergence (see eps ) number attempted iterations equal iter_max. ... arguments passed methods (currently used).","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_cph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cox regression ORSF control — orsf_control_cph","text":"object class 'orsf_control', used input control argument orsf.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_cph.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cox regression ORSF control — orsf_control_cph","text":"code survival package modified make routine. details Cox proportional hazards model, see coxph /Therneau Grambsch (2000).","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_cph.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Cox regression ORSF control — orsf_control_cph","text":"Therneau T.M., Grambsch P.M. (2000) Cox Model. : Modeling Survival Data: Extending Cox Model. Statistics Biology Health. Springer, New York, NY. DOI: 10.1007/978-1-4757-3294-8_3","code":""},{"path":[]},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_cph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cox regression ORSF control — orsf_control_cph","text":"","code":"orsf(data = pbc_orsf, formula = Surv(time, status) ~ . - id, control = orsf_control_cph()) #> ---------- Oblique random survival forest #> #> Linear combinations: Cox regression #> N observations: 276 #> N events: 111 #> N trees: 500 #> N predictors total: 17 #> N predictors per node: 5 #> Average leaves per tree: 25 #> Min observations in leaf: 5 #> Min events in leaf: 1 #> OOB stat value: 0.84 #> OOB stat type: Harrell's C-statistic #> Variable importance: anova #> #> -----------------------------------------"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_custom.html","id":null,"dir":"Reference","previous_headings":"","what":"Custom ORSF control — orsf_control_custom","title":"Custom ORSF control — orsf_control_custom","text":"Custom ORSF control","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_custom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Custom ORSF control — orsf_control_custom","text":"","code":"orsf_control_custom(beta_fun, ...)"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_custom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Custom ORSF control — orsf_control_custom","text":"beta_fun (function) function define coefficients used linear combinations predictor variables. beta_fun must accept three inputs named x_node, y_node w_node, expect following types dimensions: x_node (matrix; n rows, p columns) y_node (matrix; n rows, 2 columns) w_node (matrix; n rows, 1 column) addition, beta_fun must return matrix p rows 1 column. conditions met, orsf_control_custom() let know. ... arguments passed methods (currently used).","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_custom.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Custom ORSF control — orsf_control_custom","text":"object class 'orsf_control', used input control argument orsf.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_custom.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Custom ORSF control — orsf_control_custom","text":"Two customized functions identify linear combinations predictors shown . first uses random coefficients second derives coefficients principal component analysis.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_custom.html","id":"random-coefficients","dir":"Reference","previous_headings":"","what":"Random coefficients","title":"Custom ORSF control — orsf_control_custom","text":"f_rando() function get random coefficients: can plug f_rando orsf_control_custom(), pass result orsf():","code":"f_rando <- function(x_node, y_node, w_node){ matrix(runif(ncol(x_node)), ncol=1) } library(aorsf) fit_rando <- orsf(pbc_orsf, Surv(time, status) ~ . - id, control = orsf_control_custom(beta_fun = f_rando), n_tree = 500) fit_rando ## ---------- Oblique random survival forest ## ## Linear combinations: Custom user function ## N observations: 276 ## N events: 111 ## N trees: 500 ## N predictors total: 17 ## N predictors per node: 5 ## Average leaves per tree: 20 ## Min observations in leaf: 5 ## Min events in leaf: 1 ## OOB stat value: 0.83 ## OOB stat type: Harrell's C-statistic ## Variable importance: anova ## ## -----------------------------------------"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_custom.html","id":"principal-components","dir":"Reference","previous_headings":"","what":"Principal components","title":"Custom ORSF control — orsf_control_custom","text":"Follow steps , starting custom function: plug function orsf_control_custom() pass result orsf():","code":"f_pca <- function(x_node, y_node, w_node) { # estimate two principal components. pca <- stats::prcomp(x_node, rank. = 2) # use the second principal component to split the node pca$rotation[, 2L, drop = FALSE] } fit_pca <- orsf(pbc_orsf, Surv(time, status) ~ . - id, control = orsf_control_custom(beta_fun = f_pca), n_tree = 500)"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_custom.html","id":"evaluate","dir":"Reference","previous_headings":"","what":"Evaluate","title":"Custom ORSF control — orsf_control_custom","text":"well two customized ORSFs ? Let’s compute indices prediction accuracy based --bag predictions: PCA ORSF quite well! (higher IPA better)","code":"library(riskRegression) ## riskRegression version 2023.03.22 library(survival) risk_preds <- list(rando = 1 - fit_rando$pred_oobag, pca = 1 - fit_pca$pred_oobag) sc <- Score(object = risk_preds, formula = Surv(time, status) ~ 1, data = pbc_orsf, summary = 'IPA', times = fit_pca$pred_horizon) sc$Brier ## ## Results by model: ## ## model times Brier lower upper IPA ## <fctr> <num> <char> <char> <char> <char> ## 1: Null model 1788 20.479 18.090 22.868 0.000 ## 2: rando 1788 11.672 9.596 13.748 43.006 ## 3: pca 1788 12.917 10.885 14.950 36.924 ## ## Results of model comparisons: ## ## times model reference delta.Brier lower upper p ## <num> <fctr> <fctr> <char> <char> <char> <num> ## 1: 1788 rando Null model -8.807 -10.905 -6.709 1.896108e-16 ## 2: 1788 pca Null model -7.562 -9.235 -5.888 8.331729e-19 ## 3: 1788 pca rando 1.245 0.439 2.052 2.476657e-03 ## ## NOTE: Values are multiplied by 100 and given in %. ## NOTE: The lower Brier the better, the higher IPA the better."},{"path":[]},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_fast.html","id":null,"dir":"Reference","previous_headings":"","what":"Accelerated ORSF control — orsf_control_fast","title":"Accelerated ORSF control — orsf_control_fast","text":"Accelerated ORSF control","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_fast.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Accelerated ORSF control — orsf_control_fast","text":"","code":"orsf_control_fast(method = \"efron\", do_scale = TRUE, ...)"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_fast.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Accelerated ORSF control — orsf_control_fast","text":"method (character) character string specifying method tie handling. ties, methods equivalent. Valid options 'breslow' 'efron'. Efron approximation default accurate dealing tied event times similar computational efficiency compared Breslow method. do_scale (logical) TRUE, values predictors scaled prior instance Newton Raphson scoring, using summary values data current node decision tree. ... arguments passed methods (currently used).","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_fast.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Accelerated ORSF control — orsf_control_fast","text":"object class 'orsf_control', used input control argument orsf.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_fast.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Accelerated ORSF control — orsf_control_fast","text":"code survival package modified make routine. Adjust do_scale risk. Setting do_scale = FALSE reduce computation time also make orsf model dependent scale data, default value TRUE. good idea center scale predictors prior running orsf() plan setting do_scale = FALSE.","code":""},{"path":[]},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_fast.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Accelerated ORSF control — orsf_control_fast","text":"","code":"orsf(data = pbc_orsf, formula = Surv(time, status) ~ . - id, control = orsf_control_fast()) #> ---------- Oblique random survival forest #> #> Linear combinations: Accelerated #> N observations: 276 #> N events: 111 #> N trees: 500 #> N predictors total: 17 #> N predictors per node: 5 #> Average leaves per tree: 25 #> Min observations in leaf: 5 #> Min events in leaf: 1 #> OOB stat value: 0.84 #> OOB stat type: Harrell's C-statistic #> Variable importance: anova #> #> -----------------------------------------"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_net.html","id":null,"dir":"Reference","previous_headings":"","what":"Penalized Cox regression ORSF control — orsf_control_net","title":"Penalized Cox regression ORSF control — orsf_control_net","text":"Penalized Cox regression ORSF control","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_net.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Penalized Cox regression ORSF control — orsf_control_net","text":"","code":"orsf_control_net(alpha = 1/2, df_target = NULL, ...)"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_net.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Penalized Cox regression ORSF control — orsf_control_net","text":"alpha (double) elastic net mixing parameter. value 1 gives lasso penalty, value 0 gives ridge penalty. multiple values alpha given, penalized model fit using alpha value prior splitting node. df_target (integer) Preferred number variables used linear combination. ... arguments passed methods (currently used).","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_net.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Penalized Cox regression ORSF control — orsf_control_net","text":"object class 'orsf_control', used input control argument orsf.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_net.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Penalized Cox regression ORSF control — orsf_control_net","text":"df_target less mtry, separate argument orsf indicates number variables chosen random prior finding linear combination variables.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_net.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Penalized Cox regression ORSF control — orsf_control_net","text":"Simon N, Friedman J, Hastie T, Tibshirani R. Regularization paths Cox's proportional hazards model via coordinate descent. Journal statistical software 2011 Mar; 39(5):1. DOI: 10.18637/jss.v039.i05","code":""},{"path":[]},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_net.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Penalized Cox regression ORSF control — orsf_control_net","text":"","code":"# orsf_control_net() is considerably slower than orsf_control_cph(), # The example uses n_tree = 25 so that my examples run faster, # but you should use at least 500 trees in applied settings. orsf(data = pbc_orsf, formula = Surv(time, status) ~ . - id, n_tree = 25, control = orsf_control_net()) #> ---------- Oblique random survival forest #> #> Linear combinations: Penalized Cox regression #> N observations: 276 #> N events: 111 #> N trees: 25 #> N predictors total: 17 #> N predictors per node: 5 #> Average leaves per tree: 25 #> Min observations in leaf: 5 #> Min events in leaf: 1 #> OOB stat value: 0.84 #> OOB stat type: Harrell's C-statistic #> Variable importance: anova #> #> -----------------------------------------"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_ice_oob.html","id":null,"dir":"Reference","previous_headings":"","what":"ORSF Individual Conditional Expectations — orsf_ice_oob","title":"ORSF Individual Conditional Expectations — orsf_ice_oob","text":"Compute individual conditional expectations ORSF model. Unlike partial dependence, shows expected prediction function one multiple predictors, individual conditional expectations (ICE) show prediction individual observation function predictor. can compute individual conditional expectations three ways using random forest: using -bag predictions training data using --bag predictions training data using predictions new set data See examples details","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_ice_oob.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ORSF Individual Conditional Expectations — orsf_ice_oob","text":"","code":"orsf_ice_oob( object, pred_spec, pred_horizon = NULL, pred_type = \"risk\", expand_grid = TRUE, boundary_checks = TRUE, n_thread = 1, ... ) orsf_ice_inb( object, pred_spec, pred_horizon = NULL, pred_type = \"risk\", expand_grid = TRUE, boundary_checks = TRUE, n_thread = 1, ... ) orsf_ice_new( object, pred_spec, new_data, pred_horizon = NULL, pred_type = \"risk\", na_action = \"fail\", expand_grid = TRUE, boundary_checks = TRUE, n_thread = 1, ... )"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_ice_oob.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ORSF Individual Conditional Expectations — orsf_ice_oob","text":"object (orsf_fit) trained oblique random survival forest (see orsf). pred_spec (named list data.frame). pred_spec named list, item list vector values used points partial dependence function. name item list indicate variable modified take corresponding values. pred_spec data.frame, columns indicate variable names, values indicate variable values, partial dependence computed using inputs row. pred_horizon (double) value vector indicating time(s) predictions calibrated . E.g., predicting risk incident heart failure within next 10 years, pred_horizon = 10. pred_horizon can NULL pred_type 'mort', since mortality predictions aggregated event times pred_type (character) type predictions compute. Valid options 'risk' : probability event pred_horizon. 'surv' : 1 - risk. 'chf': cumulative hazard function 'mort': mortality prediction expand_grid (logical) TRUE, partial dependence computed possible combinations inputs pred_spec. FALSE, partial dependence computed variable pred_spec, separately. boundary_checks (logical) TRUE, pred_spec checked make sure requested values 10th 90th percentile object's training data. FALSE, checks skipped. n_thread (integer) number threads use computing predictions. Default one thread. use maximum number threads system provides concurrent execution, set n_thread = 0. ... arguments passed methods (currently used). new_data data.frame, tibble, data.table compute predictions . na_action (character) happen new_data contains missing values (.e., NA values). Valid options : 'fail' : error thrown new_data contains NA values 'omit' : rows new_data incomplete data dropped","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_ice_oob.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ORSF Individual Conditional Expectations — orsf_ice_oob","text":"data.table containing individual conditional expectations specified variable(s) specified prediction horizon(s).","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_ice_oob.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ORSF Individual Conditional Expectations — orsf_ice_oob","text":"Begin fitting ORSF ensemble Use ensemble compute ICE values using --bag predictions: Much detailed examples given vignette","code":"library(aorsf) set.seed(329) fit <- orsf(data = pbc_orsf, formula = Surv(time, status) ~ . - id) fit ## ---------- Oblique random survival forest ## ## Linear combinations: Accelerated ## N observations: 276 ## N events: 111 ## N trees: 500 ## N predictors total: 17 ## N predictors per node: 5 ## Average leaves per tree: 25 ## Min observations in leaf: 5 ## Min events in leaf: 1 ## OOB stat value: 0.84 ## OOB stat type: Harrell's C-statistic ## Variable importance: anova ## ## ----------------------------------------- pred_spec <- list(bili = seq(1, 10, length.out = 25)) ice_oob <- orsf_ice_oob(fit, pred_spec, boundary_checks = FALSE) ice_oob ## id_variable id_row pred_horizon bili pred ## <int> <fctr> <num> <num> <num> ## 1: 1 1 1788 1 0.9011797 ## 2: 1 2 1788 1 0.1096207 ## 3: 1 3 1788 1 0.7646444 ## 4: 1 4 1788 1 0.3531060 ## 5: 1 5 1788 1 0.1228441 ## --- ## 6896: 25 272 1788 10 0.3089586 ## 6897: 25 273 1788 10 0.4005430 ## 6898: 25 274 1788 10 0.4933945 ## 6899: 25 275 1788 10 0.3134373 ## 6900: 25 276 1788 10 0.5002014"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_pd_oob.html","id":null,"dir":"Reference","previous_headings":"","what":"ORSF partial dependence — orsf_pd_oob","title":"ORSF partial dependence — orsf_pd_oob","text":"Compute partial dependence ORSF model. Partial dependence (PD) shows expected prediction model function single predictor multiple predictors. expectation marginalized values predictors, giving something like multivariable adjusted estimate model's prediction. can compute partial dependence three ways using random forest: using -bag predictions training data using --bag predictions training data using predictions new set data See examples details","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_pd_oob.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ORSF partial dependence — orsf_pd_oob","text":"","code":"orsf_pd_oob( object, pred_spec, pred_horizon = NULL, pred_type = \"risk\", expand_grid = TRUE, prob_values = c(0.025, 0.5, 0.975), prob_labels = c(\"lwr\", \"medn\", \"upr\"), boundary_checks = TRUE, n_thread = 1, ... ) orsf_pd_inb( object, pred_spec, pred_horizon = NULL, pred_type = \"risk\", expand_grid = TRUE, prob_values = c(0.025, 0.5, 0.975), prob_labels = c(\"lwr\", \"medn\", \"upr\"), boundary_checks = TRUE, n_thread = 1, ... ) orsf_pd_new( object, pred_spec, new_data, pred_horizon = NULL, pred_type = \"risk\", na_action = \"fail\", expand_grid = TRUE, prob_values = c(0.025, 0.5, 0.975), prob_labels = c(\"lwr\", \"medn\", \"upr\"), boundary_checks = TRUE, n_thread = 1, ... )"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_pd_oob.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ORSF partial dependence — orsf_pd_oob","text":"object (orsf_fit) trained oblique random survival forest (see orsf). pred_spec (named list data.frame). pred_spec named list, item list vector values used points partial dependence function. name item list indicate variable modified take corresponding values. pred_spec data.frame, columns indicate variable names, values indicate variable values, partial dependence computed using inputs row. pred_horizon (double) value vector indicating time(s) predictions calibrated . E.g., predicting risk incident heart failure within next 10 years, pred_horizon = 10. pred_horizon can NULL pred_type 'mort', since mortality predictions aggregated event times pred_type (character) type predictions compute. Valid options 'risk' : probability event pred_horizon. 'surv' : 1 - risk. 'chf': cumulative hazard function 'mort': mortality prediction expand_grid (logical) TRUE, partial dependence computed possible combinations inputs pred_spec. FALSE, partial dependence computed variable pred_spec, separately. prob_values (numeric) vector values 0 1, indicating quantiles used summarize partial dependence values set inputs. prob_values length prob_labels. quantiles calculated based predictions object set values indicated pred_spec. prob_labels (character) vector labels length prob_values, label indicating corresponding value prob_values labelled summarized outputs. prob_labels length prob_values. boundary_checks (logical) TRUE, pred_spec checked make sure requested values 10th 90th percentile object's training data. FALSE, checks skipped. n_thread (integer) number threads use computing predictions. Default one thread. use maximum number threads system provides concurrent execution, set n_thread = 0. ... arguments passed methods (currently used). new_data data.frame, tibble, data.table compute predictions . na_action (character) happen new_data contains missing values (.e., NA values). Valid options : 'fail' : error thrown new_data contains NA values 'omit' : rows new_data incomplete data dropped","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_pd_oob.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ORSF partial dependence — orsf_pd_oob","text":"data.table containing partial dependence values specified variable(s) specified prediction horizon(s).","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_pd_oob.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ORSF partial dependence — orsf_pd_oob","text":"Partial dependence number known limitations assumptions users aware (see Hooker, 2021). particular, partial dependence less intuitive >2 predictors examined jointly, assumed feature(s) partial dependence computed correlated features (likely true many cases). Accumulated local effect plots can used (see ) case feature independence valid assumption.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_pd_oob.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ORSF partial dependence — orsf_pd_oob","text":"Begin fitting ORSF ensemble:","code":"library(aorsf) set.seed(329730) index_train <- sample(nrow(pbc_orsf), 150) pbc_orsf_train <- pbc_orsf[index_train, ] pbc_orsf_test <- pbc_orsf[-index_train, ] fit <- orsf(data = pbc_orsf_train, formula = Surv(time, status) ~ . - id, oobag_pred_horizon = 365.25 * 5)"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_pd_oob.html","id":"three-ways-to-compute-pd-and-ice","dir":"Reference","previous_headings":"","what":"Three ways to compute PD and ICE","title":"ORSF partial dependence — orsf_pd_oob","text":"can compute partial dependence ICE three ways aorsf: using -bag predictions training data using --bag predictions training data using predictions new set data -bag partial dependence indicates relationships model learned training. helpful goal interpret model. --bag partial dependence indicates relationships model learned training using --bag data simulates application model new data. want test model’s reliability fairness new data don’t access large testing set. new data partial dependence shows model predicts outcomes observations seen. helpful want test model’s reliability fairness.","code":"pd_train <- orsf_pd_inb(fit, pred_spec = list(bili = 1:5)) pd_train ## pred_horizon bili mean lwr medn upr ## <num> <num> <num> <num> <num> <num> ## 1: 1826.25 1 0.2151663 0.02028479 0.09634648 0.7997269 ## 2: 1826.25 2 0.2576618 0.03766695 0.15497447 0.8211875 ## 3: 1826.25 3 0.2998484 0.06436773 0.20771324 0.8425637 ## 4: 1826.25 4 0.3390664 0.08427149 0.25401067 0.8589590 ## 5: 1826.25 5 0.3699045 0.10650098 0.28284427 0.8689855 pd_train <- orsf_pd_oob(fit, pred_spec = list(bili = 1:5)) pd_train ## pred_horizon bili mean lwr medn upr ## <num> <num> <num> <num> <num> <num> ## 1: 1826.25 1 0.2145044 0.01835000 0.09619052 0.7980629 ## 2: 1826.25 2 0.2566241 0.03535358 0.14185734 0.8173143 ## 3: 1826.25 3 0.2984693 0.05900059 0.20515477 0.8334243 ## 4: 1826.25 4 0.3383547 0.07887323 0.24347513 0.8469769 ## 5: 1826.25 5 0.3696260 0.10450534 0.28065473 0.8523756 pd_test <- orsf_pd_new(fit, new_data = pbc_orsf_test, pred_spec = list(bili = 1:5)) pd_test ## pred_horizon bili mean lwr medn upr ## <num> <num> <num> <num> <num> <num> ## 1: 1826.25 1 0.2542230 0.02901386 0.1943767 0.8143912 ## 2: 1826.25 2 0.2955726 0.05037316 0.2474559 0.8317684 ## 3: 1826.25 3 0.3388434 0.07453896 0.3010898 0.8488622 ## 4: 1826.25 4 0.3800254 0.10565022 0.3516805 0.8592057 ## 5: 1826.25 5 0.4124587 0.12292465 0.3915066 0.8690074"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_pd_oob.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"ORSF partial dependence — orsf_pd_oob","text":"Giles Hooker, Lucas Mentch, Siyu Zhou. Unrestricted Permutation forces Extrapolation: Variable Importance Requires least One Model, Free Variable Importance. arXiv e-prints 2021 Oct; arXiv-1905. URL: https://doi.org/10.48550/arXiv.1905.03151","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_scale_cph.html","id":null,"dir":"Reference","previous_headings":"","what":"Scale input data — orsf_scale_cph","title":"Scale input data — orsf_scale_cph","text":"functions exported users may access internal routines used scale inputs orsf_control_cph used.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_scale_cph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scale input data — orsf_scale_cph","text":"","code":"orsf_scale_cph(x_mat, w_vec = NULL) orsf_unscale_cph(x_mat)"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_scale_cph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scale input data — orsf_scale_cph","text":"x_mat (numeric matrix) matrix values scaled unscaled. Note orsf_unscale_cph accept x_mat inputs attribute containing transform values, added automatically orsf_scale_cph. w_vec (numeric vector) optional vector weights. weights supplied (default), observations equally weighted. supplied, w_vec must length equal nrow(x_mat).","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_scale_cph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scale input data — orsf_scale_cph","text":"scaled unscaled x_mat.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_scale_cph.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Scale input data — orsf_scale_cph","text":"data transformed first subtracting mean multiplying scale. inverse transform can completed using orsf_unscale_cph dividing column corresponding scale adding mean. values means scales stored attribute output returned orsf_scale_cph (see examples)","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_scale_cph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scale input data — orsf_scale_cph","text":"","code":"x_mat <- as.matrix(pbc_orsf[, c('bili', 'age', 'protime')]) head(x_mat) #> bili age protime #> 1 14.5 58.76523 12.2 #> 2 1.1 56.44627 10.6 #> 3 1.4 70.07255 12.0 #> 4 1.8 54.74059 10.3 #> 5 3.4 38.10541 10.9 #> 7 1.0 55.53457 9.7 x_scaled <- orsf_scale_cph(x_mat) head(x_scaled) #> bili age protime #> [1,] 3.77308887 1.0412574 1.9694656 #> [2,] -0.75476469 0.7719344 -0.1822316 #> [3,] -0.65339483 2.3544852 1.7005035 #> [4,] -0.51823502 0.5738373 -0.5856748 #> [5,] 0.02240421 -1.3581657 0.2212116 #> [6,] -0.78855464 0.6660494 -1.3925613 attributes(x_scaled) # note the transforms attribute #> $dim #> [1] 276 3 #> #> $dimnames #> $dimnames[[1]] #> NULL #> #> $dimnames[[2]] #> [1] \"bili\" \"age\" \"protime\" #> #> #> $transforms #> mean scale #> [1,] 3.333696 0.3378995 #> [2,] 49.799661 0.1161396 #> [3,] 10.735507 1.3448108 #> x_unscaled <- orsf_unscale_cph(x_scaled) head(x_unscaled) #> bili age protime #> [1,] 14.5 58.76523 12.2 #> [2,] 1.1 56.44627 10.6 #> [3,] 1.4 70.07255 12.0 #> [4,] 1.8 54.74059 10.3 #> [5,] 3.4 38.10541 10.9 #> [6,] 1.0 55.53457 9.7 # numeric difference in x_mat and x_unscaled should be practically 0 max(abs(x_mat - x_unscaled)) #> [1] 8.881784e-16"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_summarize_uni.html","id":null,"dir":"Reference","previous_headings":"","what":"ORSF summary; univariate — orsf_summarize_uni","title":"ORSF summary; univariate — orsf_summarize_uni","text":"Summarize univariate information ORSF object","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_summarize_uni.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ORSF summary; univariate — orsf_summarize_uni","text":"","code":"orsf_summarize_uni( object, n_variables = NULL, pred_horizon = NULL, pred_type = \"risk\", importance = \"negate\", ... )"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_summarize_uni.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ORSF summary; univariate — orsf_summarize_uni","text":"object (orsf_fit) trained oblique random survival forest (see orsf). n_variables (integer) many variables summarized? Setting input lower number reduce computation time. pred_horizon (double) value vector indicating time(s) predictions calibrated . E.g., predicting risk incident heart failure within next 10 years, pred_horizon = 10. pred_horizon can NULL pred_type 'mort', since mortality predictions aggregated event times pred_type (character) type predictions compute. Valid options 'risk' : probability event pred_horizon. 'surv' : 1 - risk. 'chf': cumulative hazard function 'mort': mortality prediction importance (character) Indicate method variable importance: 'none': variable importance computed. 'anova': compute analysis variance (ANOVA) importance 'negate': compute negation importance 'permute': compute permutation importance details methods, see orsf_vi. ... arguments passed methods (currently used).","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_summarize_uni.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ORSF summary; univariate — orsf_summarize_uni","text":"object class 'orsf_summary', includes data importance individual predictors. expected values predictions specific values predictors.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_summarize_uni.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ORSF summary; univariate — orsf_summarize_uni","text":"pred_horizon left unspecified, median value time--event variable object's training data used. recommended always specify prediction horizon, median time may especially meaningful horizon compute predicted risk values . object already variable importance values, can safely bypass computation variable importance function setting importance = 'none'.","code":""},{"path":[]},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_summarize_uni.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ORSF summary; univariate — orsf_summarize_uni","text":"","code":"object <- orsf(pbc_orsf, Surv(time, status) ~ . - id) # since anova importance was used to make object, we can # safely say importance = 'none' and skip computation of # variable importance while running orsf_summarize_uni orsf_summarize_uni(object, n_variables = 3, importance = 'none') #> #> -- ascites (VI Rank: 1) ------------------------ #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 0 0.3056596 0.1604080 0.05403967 0.5549432 #> 1 0.4541163 0.3720453 0.24575548 0.6537995 #> #> -- edema (VI Rank: 2) -------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 0 0.3006362 0.1597512 0.05229420 0.5519929 #> 0.5 0.3623431 0.2439360 0.09813076 0.6174363 #> 1 0.4563040 0.3679923 0.24789758 0.6588647 #> #> -- bili (VI Rank: 3) --------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 0.80 0.2396105 0.1322078 0.05552047 0.3900583 #> 1.4 0.2667532 0.1579549 0.07450011 0.4186372 #> 3.5 0.3754583 0.2833106 0.16142246 0.5699637 #> #> Predicted risk at time t = 1788 for top 3 predictors # however, if we want to summarize object according to variables # ranked by negation importance, we can compute negation importance # within orsf_summarize_uni() as follows: orsf_summarize_uni(object, n_variables = 3, importance = 'negate') #> #> -- bili (VI Rank: 1) --------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 0.80 0.2396105 0.1322078 0.05552047 0.3900583 #> 1.4 0.2667532 0.1579549 0.07450011 0.4186372 #> 3.5 0.3754583 0.2833106 0.16142246 0.5699637 #> #> -- copper (VI Rank: 2) ------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 43 0.2703663 0.1485739 0.05372852 0.4547391 #> 74 0.2964628 0.1714574 0.06789143 0.4963866 #> 129 0.3534530 0.2487902 0.12636325 0.5775581 #> #> -- sex (VI Rank: 3) ---------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> m 0.3666038 0.2542865 0.1260373 0.5933671 #> f 0.3048034 0.1532173 0.0522942 0.5401139 #> #> Predicted risk at time t = 1788 for top 3 predictors"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_time_to_train.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate training time — orsf_time_to_train","title":"Estimate training time — orsf_time_to_train","text":"Estimate training time","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_time_to_train.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate training time — orsf_time_to_train","text":"","code":"orsf_time_to_train(object, n_tree_subset = 50)"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_time_to_train.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate training time — orsf_time_to_train","text":"object untrained aorsf object n_tree_subset (integer) many trees fit order estimate time needed train object. default value 50, usually gives good enough approximation.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_time_to_train.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate training time — orsf_time_to_train","text":"difftime object.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_time_to_train.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate training time — orsf_time_to_train","text":"","code":"# specify but do not train the model by setting no_fit = TRUE. object <- orsf(pbc_orsf, Surv(time, status) ~ . - id, n_tree = 500, no_fit = TRUE) # grow 50 trees to approximate the time it will take to grow 500 trees time_estimated <- orsf_time_to_train(object, n_tree_subset = 50) print(time_estimated) #> Time difference of 0.2371516 secs # let's see how close the approximation was time_true_start <- Sys.time() fit <- orsf_train(object) time_true_stop <- Sys.time() time_true <- time_true_stop - time_true_start print(time_true) #> Time difference of 0.2216668 secs # error abs(time_true - time_estimated) #> Time difference of 0.01548481 secs"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vi.html","id":null,"dir":"Reference","previous_headings":"","what":"ORSF variable importance — orsf_vi","title":"ORSF variable importance — orsf_vi","text":"Estimate importance individual variables using oblique random survival forests.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ORSF variable importance — orsf_vi","text":"","code":"orsf_vi( object, group_factors = TRUE, importance = NULL, oobag_fun = NULL, n_thread = 1, verbose_progress = FALSE, ... ) orsf_vi_negate( object, group_factors = TRUE, oobag_fun = NULL, n_thread = 1, verbose_progress = FALSE, ... ) orsf_vi_permute( object, group_factors = TRUE, oobag_fun = NULL, n_thread = 1, verbose_progress = FALSE, ... ) orsf_vi_anova(object, group_factors = TRUE, ...)"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ORSF variable importance — orsf_vi","text":"object (orsf_fit) trained oblique random survival forest (see orsf). group_factors (logical) TRUE, importance factor variables reported overall aggregating importance individual levels factor. FALSE, importance individual factor levels returned. importance (character) Indicate method variable importance: 'anova': compute analysis variance (ANOVA) importance 'negate': compute negation importance 'permute': compute permutation importance oobag_fun (function) used evaluating --bag prediction accuracy negating coefficients (importance = 'negate') permuting values predictor (importance = 'permute') oobag_fun = NULL (default), Harrell's C-statistic (1982) used evaluate accuracy. use oobag_fun note following: oobag_fun two inputs: y_mat s_vec y_mat two column matrix first column named 'time', second named 'status' s_vec numeric vector containing predicted survival probabilities. oobag_fun return numeric output length 1 oobag_fun used created object initial value --bag prediction accuracy consistent values computed variable importance estimated. details, see --bag vignette. n_thread (integer) number threads use computing predictions. Default one thread. use maximum number threads system provides concurrent execution, set n_thread = 0. verbose_progress (logical) TRUE, progress messages printed console. FALSE (default), nothing printed. ... arguments passed methods (currently used).","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ORSF variable importance — orsf_vi","text":"orsf_vi functions return named numeric vector. Names vector predictor variables used object Values vector estimated importance given predictor. returned vector sorted highest lowest value, higher values indicating higher importance.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ORSF variable importance — orsf_vi","text":"orsf_fit object fitted importance = 'anova', 'negate', 'permute', output vector importance values based requested type importance. However, may still want call orsf_vi() output want group factor levels one overall importance value. orsf_vi() general purpose function extract compute variable importance estimates 'orsf_fit' object (see orsf). orsf_vi_negate(), orsf_vi_permute(), orsf_vi_anova() wrappers orsf_vi(). way functions work depends whether object given already variable importance estimates (see examples).","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vi.html","id":"variable-importance-methods","dir":"Reference","previous_headings":"","what":"Variable importance methods","title":"ORSF variable importance — orsf_vi","text":"negation importance: variable assessed separately multiplying variable's coefficients -1 determining much model's performance changes. worse model's performance negating coefficients given variable, important variable. technique promising b/c require permutation emphasizes variables larger coefficients linear combinations, also relatively new studied much permutation importance. See Jaeger, 2022 details technique. permutation importance: variable assessed separately randomly permuting variable's values determining much model's performance changes. worse model's performance permuting values given variable, important variable. technique flexible, intuitive, frequently used. also several known limitations analysis variance (ANOVA) importance: p-value computed coefficient linear combination variables decision tree. Importance individual predictor variable proportion times p-value coefficient < 0.01. technique efficient computationally, may effective permutation negation terms selecting signal noise variables. See Menze, 2011 details technique.","code":""},{"path":[]},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vi.html","id":"anova-importance","dir":"Reference","previous_headings":"","what":"ANOVA importance","title":"ORSF variable importance — orsf_vi","text":"default variable importance technique, ANOVA, calculated fit ORSF ensemble. ANOVA default fast, may decisive permutation negation techniques variable selection.","code":"fit <- orsf(pbc_orsf, Surv(time, status) ~ . - id) fit ## ---------- Oblique random survival forest ## ## Linear combinations: Accelerated ## N observations: 276 ## N events: 111 ## N trees: 500 ## N predictors total: 17 ## N predictors per node: 5 ## Average leaves per tree: 25 ## Min observations in leaf: 5 ## Min events in leaf: 1 ## OOB stat value: 0.84 ## OOB stat type: Harrell's C-statistic ## Variable importance: anova ## ## -----------------------------------------"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vi.html","id":"raw-vi-values","dir":"Reference","previous_headings":"","what":"Raw VI values","title":"ORSF variable importance — orsf_vi","text":"‘raw’ variable importance values can accessed fit object ‘raw’ values factors aggregated single value. Currently one value k-1 levels k level factor. example, can see edema_1 edema_0.5 importance values edema factor variable levels 0, 0.5, 1.","code":"attr(fit, 'importance_values') ## ascites_1 edema_1 bili albumin copper edema_0.5 ## 0.44146501 0.43190921 0.29391304 0.22145499 0.22120519 0.20110957 ## age protime chol spiders_1 stage sex_f ## 0.19980193 0.19329637 0.17777778 0.17772293 0.16048729 0.15926709 ## hepato_1 ast trig alk.phos platelet trt_placebo ## 0.15816481 0.15734785 0.13200993 0.12433796 0.11844461 0.09404636"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vi.html","id":"collapse-vi-across-factor-levels","dir":"Reference","previous_headings":"","what":"Collapse VI across factor levels","title":"ORSF variable importance — orsf_vi","text":"get aggregated values across levels factor, access importance element orsf fit: use orsf_vi() group_factors set TRUE (default) Note can make default returned importance values ungrouped setting group_factors FALSE orsf_vi functions orsf function.","code":"fit$importance ## ascites edema bili albumin copper age protime ## 0.44146501 0.29452847 0.29391304 0.22145499 0.22120519 0.19980193 0.19329637 ## chol spiders stage sex hepato ast trig ## 0.17777778 0.17772293 0.16048729 0.15926709 0.15816481 0.15734785 0.13200993 ## alk.phos platelet trt ## 0.12433796 0.11844461 0.09404636 orsf_vi(fit) ## ascites edema bili albumin copper age protime ## 0.44146501 0.29452847 0.29391304 0.22145499 0.22120519 0.19980193 0.19329637 ## chol spiders stage sex hepato ast trig ## 0.17777778 0.17772293 0.16048729 0.15926709 0.15816481 0.15734785 0.13200993 ## alk.phos platelet trt ## 0.12433796 0.11844461 0.09404636"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vi.html","id":"add-vi-to-an-orsf","dir":"Reference","previous_headings":"","what":"Add VI to an ORSF","title":"ORSF variable importance — orsf_vi","text":"can fit ORSF without VI, add VI later","code":"fit_no_vi <- orsf(pbc_orsf, Surv(time, status) ~ . - id, importance = 'none') # Note: you can't call orsf_vi_anova() on fit_no_vi because anova # VI can only be computed while the forest is being grown. orsf_vi_negate(fit_no_vi) ## bili copper sex protime stage ## 0.1139657923 0.0498712200 0.0355366377 0.0283554322 0.0263792287 ## albumin age ascites chol ast ## 0.0231636378 0.0195791833 0.0175120075 0.0148252414 0.0104918262 ## edema spiders hepato trt trig ## 0.0084871358 0.0070608860 0.0067054788 0.0052040792 0.0030363455 ## alk.phos platelet ## 0.0029918139 -0.0003309069 orsf_vi_permute(fit_no_vi) ## bili copper protime albumin stage ## 0.0511641625 0.0244676999 0.0160869571 0.0133334120 0.0130092352 ## ascites age hepato edema chol ## 0.0127421184 0.0113532728 0.0050851329 0.0050477457 0.0049382275 ## ast spiders sex alk.phos trig ## 0.0047345189 0.0038719163 0.0025231267 0.0018408350 0.0011528848 ## platelet trt ## -0.0002875319 -0.0024330707"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vi.html","id":"orsf-and-vi-all-at-once","dir":"Reference","previous_headings":"","what":"ORSF and VI all at once","title":"ORSF variable importance — orsf_vi","text":"fit ORSF compute vi time can still get negation VI fit, needs computed","code":"fit_permute_vi <- orsf(pbc_orsf, Surv(time, status) ~ . - id, importance = 'permute') # get the vi instantly (i.e., it doesn't need to be computed again) orsf_vi_permute(fit_permute_vi) ## bili copper age albumin protime ## 0.0502725526 0.0201473283 0.0135888938 0.0127241082 0.0126629150 ## stage ascites ast edema chol ## 0.0124866976 0.0123508555 0.0060741690 0.0059166139 0.0053767371 ## spiders sex hepato trig alk.phos ## 0.0042600602 0.0028177750 0.0023470782 0.0021331719 0.0016874102 ## platelet trt ## 0.0002117061 -0.0005790547 orsf_vi_negate(fit_permute_vi) ## bili copper sex stage age protime ## 0.1106715167 0.0456031656 0.0306666098 0.0304383573 0.0252136203 0.0224838590 ## albumin ascites chol ast edema trt ## 0.0212630703 0.0168893963 0.0134174671 0.0132075752 0.0099681058 0.0088378768 ## spiders hepato trig alk.phos platelet ## 0.0078776082 0.0062877323 0.0043076141 0.0030432581 0.0005571111"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vi.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"ORSF variable importance — orsf_vi","text":"Harrell FE, Califf RM, Pryor DB, Lee KL, Rosati RA. Evaluating Yield Medical Tests. JAMA 1982; 247(18):2543-2546. DOI: 10.1001/jama.1982.03320430047030 Breiman L. Random forests. Machine learning 2001 Oct; 45(1):5-32. DOI: 10.1023/:1010933404324 Menze BH, Kelm BM, Splitthoff DN, Koethe U, Hamprecht FA. oblique random forests. Joint European Conference Machine Learning Knowledge Discovery Databases 2011 Sep 4; pp. 453-469. DOI: 10.1007/978-3-642-23783-6_29 Jaeger BC, Welden S, Lenoir K, Speiser JL, Segar MW, Pandey , Pajewski NM. Accelerated interpretable oblique random survival forests. arXiv e-prints 2022 Aug; arXiv-2208. URL: https://arxiv.org/abs/2208.01129","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vs.html","id":null,"dir":"Reference","previous_headings":"","what":"Variable selection — orsf_vs","title":"Variable selection — orsf_vs","text":"Variable selection","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variable selection — orsf_vs","text":"","code":"orsf_vs(object, n_predictor_min = 3, verbose_progress = FALSE)"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variable selection — orsf_vs","text":"object (orsf_fit) trained oblique random survival forest (see orsf). n_predictor_min (integer) minimum number predictors allowed verbose_progress (logical) implemented yet. progress printed console?","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Variable selection — orsf_vs","text":"data.table four columns: n_predictors: number predictors used stat_value: --bag statistic predictors_included: names predictors included predictor_dropped: predictor selected dropped","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Variable selection — orsf_vs","text":"tree_seeds specified object successive run orsf evaluated --bag samples initial run.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Variable selection — orsf_vs","text":"","code":"object <- orsf(formula = time + status ~ ., data = pbc_orsf, n_tree = 25, importance = 'anova', tree_seeds = 1:25) orsf_vs(object) #> n_predictors stat_value predictors_included #> 1: 3 0.7934788 ascites,edema,bili #> 2: 4 0.8156154 age,ascites,edema,bili #> 3: 5 0.8205115 age,ascites,edema,bili,copper #> 4: 6 0.8297568 age,ascites,edema,bili,chol,copper #> 5: 7 0.8189749 age,ascites,hepato,edema,bili,chol,... #> 6: 8 0.8296526 age,ascites,hepato,spiders,edema,bili,... #> 7: 9 0.8257201 age,sex,ascites,hepato,spiders,edema,... #> 8: 10 0.8252253 age,sex,ascites,hepato,spiders,edema,... #> 9: 11 0.8225168 age,sex,ascites,hepato,spiders,edema,... #> 10: 12 0.8095474 age,sex,ascites,hepato,spiders,edema,... #> 11: 13 0.8236887 age,sex,ascites,hepato,spiders,edema,... #> 12: 14 0.8277514 age,sex,ascites,hepato,spiders,edema,... #> 13: 15 0.8134017 age,sex,ascites,hepato,spiders,edema,... #> 14: 16 0.8393666 age,sex,ascites,hepato,spiders,edema,... #> 15: 17 0.8200167 id,age,sex,ascites,hepato,spiders,... #> 16: 18 0.8119954 id,trt,age,sex,ascites,hepato,... #> predictor_dropped #> 1: ascites #> 2: age #> 3: copper #> 4: chol #> 5: hepato #> 6: spiders #> 7: sex #> 8: albumin #> 9: ast #> 10: protime #> 11: alk.phos #> 12: stage #> 13: platelet #> 14: trig #> 15: id #> 16: trt"},{"path":"https://bcjaeger.github.io/aorsf/reference/pbc_orsf.html","id":null,"dir":"Reference","previous_headings":"","what":"Mayo Clinic Primary Biliary Cholangitis Data — pbc_orsf","title":"Mayo Clinic Primary Biliary Cholangitis Data — pbc_orsf","text":"data light modification survival::pbc data. modifications :","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/pbc_orsf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mayo Clinic Primary Biliary Cholangitis Data — pbc_orsf","text":"","code":"pbc_orsf"},{"path":"https://bcjaeger.github.io/aorsf/reference/pbc_orsf.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Mayo Clinic Primary Biliary Cholangitis Data — pbc_orsf","text":"data frame 276 rows 20 variables: id case number time number days registration earlier death, transplantion, study analysis July, 1986 status status endpoint, 0 censored transplant, 1 dead trt randomized treatment group: D-penicillmain placebo age years sex m/f ascites presence ascites hepato presence hepatomegaly enlarged liver spiders blood vessel malformations skin edema 0 edema, 0.5 untreated successfully treated, 1 edema despite diuretic therapy bili serum bilirubin (mg/dl) chol serum cholesterol (mg/dl) albumin serum albumin (g/dl) copper urine copper (ug/day) alk.phos alkaline phosphotase (U/liter) ast aspartate aminotransferase, called SGOT (U/ml) trig triglycerides (mg/dl) platelet platelet count protime standardized blood clotting time stage histologic stage disease (needs biopsy)","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/pbc_orsf.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Mayo Clinic Primary Biliary Cholangitis Data — pbc_orsf","text":"T Therneau P Grambsch (2000), Modeling Survival Data: Extending Cox Model, Springer-Verlag, New York. ISBN: 0-387-98784-3.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/pbc_orsf.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Mayo Clinic Primary Biliary Cholangitis Data — pbc_orsf","text":"removed rows missing data converted status 0 censor transplant, 1 dead converted stage ordered factor. converted trt, ascites, hepato, spiders, edema factors.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/predict.orsf_fit.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute predictions using ORSF — predict.orsf_fit","title":"Compute predictions using ORSF — predict.orsf_fit","text":"Predicted risk, survival, hazard, mortality ORSF model.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/predict.orsf_fit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute predictions using ORSF — predict.orsf_fit","text":"","code":"# S3 method for orsf_fit predict( object, new_data, pred_horizon = NULL, pred_type = \"risk\", na_action = \"fail\", boundary_checks = TRUE, n_thread = 1, verbose_progress = FALSE, pred_aggregate = TRUE, ... )"},{"path":"https://bcjaeger.github.io/aorsf/reference/predict.orsf_fit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute predictions using ORSF — predict.orsf_fit","text":"object (orsf_fit) trained oblique random survival forest (see orsf). new_data data.frame, tibble, data.table compute predictions . pred_horizon (double) value vector indicating time(s) predictions calibrated . E.g., predicting risk incident heart failure within next 10 years, pred_horizon = 10. pred_horizon can NULL pred_type 'mort', since mortality predictions aggregated event times pred_type (character) type predictions compute. Valid options 'risk' : probability event pred_horizon. 'surv' : 1 - risk. 'chf': cumulative hazard function 'mort': mortality prediction na_action (character) happen new_data contains missing values (.e., NA values). Valid options : 'fail' : error thrown new_data contains NA values 'pass' : output NA rows new_data 1 NA value predictors used object 'omit' : rows new_data incomplete data dropped 'impute_meanmode' : missing values continuous categorical variables new_data imputed using mean mode, respectively. clarify, mean mode used impute missing values training data object, new_data. boundary_checks (logical) TRUE, pred_horizon checked make sure requested values less maximum observed time object's training data. FALSE, checks skipped. n_thread (integer) number threads use computing predictions. Default one thread. use maximum number threads system provides concurrent execution, set n_thread = 0. verbose_progress (logical) TRUE, progress messages printed console. FALSE (default), nothing printed. pred_aggregate (logical) TRUE (default), predictions aggregated trees taking mean. FALSE, returned output contain one row per observation one column tree. length pred_horizon two pred_aggregate FALSE, result list matrices, 'th item list corresponding 'th value pred_horizon. ... arguments passed methods (currently used).","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/predict.orsf_fit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute predictions using ORSF — predict.orsf_fit","text":"matrix predictions. Column j matrix corresponds value j pred_horizon. Row matrix corresponds row new_data.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/predict.orsf_fit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute predictions using ORSF — predict.orsf_fit","text":"new_data must columns equivalent types data used train object. Also, factors new_data must levels data used train object. pred_horizon values exceed maximum follow-time object's training data, truly want , set boundary_checks = FALSE can use pred_horizon large want. Note predictions beyond maximum follow-time object's training data equal predictions maximum follow-time, aorsf estimate survival beyond maximum observed time. unspecified, pred_horizon may automatically specified value used oobag_pred_horizon object created (see orsf).","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/predict.orsf_fit.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute predictions using ORSF — predict.orsf_fit","text":"Begin fitting ORSF ensemble: Predict risk, survival, cumulative hazard one several times: Predict mortality, defined number events forest’s population observations characteristics like current observation. type prediction require specify prediction horizon","code":"library(aorsf) set.seed(329730) index_train <- sample(nrow(pbc_orsf), 150) pbc_orsf_train <- pbc_orsf[index_train, ] pbc_orsf_test <- pbc_orsf[-index_train, ] fit <- orsf(data = pbc_orsf_train, formula = Surv(time, status) ~ . - id, oobag_pred_horizon = 365.25 * 5) # predicted risk, the default predict(fit, new_data = pbc_orsf_test[1:5, ], pred_type = 'risk', pred_horizon = c(500, 1000, 1500)) ## [,1] [,2] [,3] ## [1,] 0.49679905 0.77309053 0.90830168 ## [2,] 0.03363621 0.08527972 0.17061414 ## [3,] 0.15129784 0.30402666 0.43747212 ## [4,] 0.01152480 0.02950914 0.07068198 ## [5,] 0.01035341 0.01942262 0.05117679 # predicted survival, i.e., 1 - risk predict(fit, new_data = pbc_orsf_test[1:5, ], pred_type = 'surv', pred_horizon = c(500, 1000, 1500)) ## [,1] [,2] [,3] ## [1,] 0.5032009 0.2269095 0.09169832 ## [2,] 0.9663638 0.9147203 0.82938586 ## [3,] 0.8487022 0.6959733 0.56252788 ## [4,] 0.9884752 0.9704909 0.92931802 ## [5,] 0.9896466 0.9805774 0.94882321 # predicted cumulative hazard function # (expected number of events for person i at time j) predict(fit, new_data = pbc_orsf_test[1:5, ], pred_type = 'chf', pred_horizon = c(500, 1000, 1500)) ## [,1] [,2] [,3] ## [1,] 0.74442414 1.39538511 1.78344589 ## [2,] 0.03473938 0.10418984 0.24047328 ## [3,] 0.19732086 0.47015754 0.73629459 ## [4,] 0.01169147 0.03223257 0.09564168 ## [5,] 0.01072007 0.02240040 0.06464319 predict(fit, new_data = pbc_orsf_test[1:5, ], pred_type = 'mort') ## [,1] ## [1,] 83.08611 ## [2,] 27.48146 ## [3,] 43.52432 ## [4,] 15.20281 ## [5,] 10.56334"},{"path":"https://bcjaeger.github.io/aorsf/reference/print.orsf_fit.html","id":null,"dir":"Reference","previous_headings":"","what":"Inspect your ORSF model — print.orsf_fit","title":"Inspect your ORSF model — print.orsf_fit","text":"Printing ORSF model tells : Linear combinations: identified? N observations: Number rows training data N events: Number events training data N trees: Number trees forest N predictors total: Total number columns predictor matrix N predictors per node: Number variables used linear combinations Average leaves per tree: proxy depth trees Min observations leaf: See leaf_min_obs orsf Min events leaf: See leaf_min_events orsf OOB stat value: --bag error fitting trees OOB stat type: --bag error computed? Variable importance: variable importance computed?","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/print.orsf_fit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inspect your ORSF model — print.orsf_fit","text":"","code":"# S3 method for orsf_fit print(x, ...)"},{"path":"https://bcjaeger.github.io/aorsf/reference/print.orsf_fit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inspect your ORSF model — print.orsf_fit","text":"x (orsf_fit) oblique random survival forest (ORSF; see orsf). ... arguments passed methods (currently used).","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/print.orsf_fit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inspect your ORSF model — print.orsf_fit","text":"x, invisibly.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/print.orsf_fit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Inspect your ORSF model — print.orsf_fit","text":"","code":"object <- orsf(pbc_orsf, Surv(time, status) ~ . - id, n_tree = 5) print(object) #> ---------- Oblique random survival forest #> #> Linear combinations: Accelerated #> N observations: 276 #> N events: 111 #> N trees: 5 #> N predictors total: 17 #> N predictors per node: 5 #> Average leaves per tree: 25 #> Min observations in leaf: 5 #> Min events in leaf: 1 #> OOB stat value: 0.74 #> OOB stat type: Harrell's C-statistic #> Variable importance: anova #> #> -----------------------------------------"},{"path":"https://bcjaeger.github.io/aorsf/reference/print.orsf_summary_uni.html","id":null,"dir":"Reference","previous_headings":"","what":"Print ORSF summary — print.orsf_summary_uni","title":"Print ORSF summary — print.orsf_summary_uni","text":"Print ORSF summary","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/print.orsf_summary_uni.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print ORSF summary — print.orsf_summary_uni","text":"","code":"# S3 method for orsf_summary_uni print(x, n_variables = NULL, ...)"},{"path":"https://bcjaeger.github.io/aorsf/reference/print.orsf_summary_uni.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print ORSF summary — print.orsf_summary_uni","text":"x object class 'orsf_summary' n_variables number variables print ... arguments passed methods (currently used).","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/print.orsf_summary_uni.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print ORSF summary — print.orsf_summary_uni","text":"invisibly, x","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/print.orsf_summary_uni.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print ORSF summary — print.orsf_summary_uni","text":"","code":"object <- orsf(pbc_orsf, Surv(time, status) ~ . - id) smry <- orsf_summarize_uni(object, n_variables = 3) print(smry) #> #> -- bili (VI Rank: 1) --------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 0.80 0.2372302 0.1290978 0.04988573 0.3759563 #> 1.4 0.2637580 0.1564218 0.06983125 0.4189478 #> 3.5 0.3769927 0.2961613 0.17078560 0.5771778 #> #> -- copper (VI Rank: 2) ------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 43 0.2687370 0.1562238 0.05255161 0.4302762 #> 74 0.2906106 0.1715181 0.06366127 0.4734150 #> 129 0.3488795 0.2561287 0.11884521 0.5729055 #> #> -- sex (VI Rank: 3) ---------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> m 0.3638092 0.2579718 0.12172678 0.5956792 #> f 0.3042276 0.1688181 0.05253388 0.5343637 #> #> Predicted risk at time t = 1788 for top 3 predictors"},{"path":"https://bcjaeger.github.io/aorsf/news/index.html","id":"aorsf-010-unreleased","dir":"Changelog","previous_headings":"","what":"aorsf 0.1.0 (unreleased)","title":"aorsf 0.1.0 (unreleased)","text":"Re-worked aorsf’s C++, code following design ranger, set classification regression trees. Allowed multi-threading performed orsf(), predict.orsf_fit(), functions orsf_vi() orsf_pd() family. Allowed sampling without replacement sampling specific fraction observations orsf() Included Harrell’s C-statistic option assessing goodness splits growing trees. Fixed issue uninformative error message occur pred_horizon > max(time) orsf_summarize_uni. Thanks @JyHao1 @DustinMLong finding !","code":""},{"path":"https://bcjaeger.github.io/aorsf/news/index.html","id":"aorsf-007","dir":"Changelog","previous_headings":"","what":"aorsf 0.0.7","title":"aorsf 0.0.7","text":"CRAN release: 2023-01-12 Additional changes internal testing avoid problems ATLAS","code":""},{"path":"https://bcjaeger.github.io/aorsf/news/index.html","id":"aorsf-006","dir":"Changelog","previous_headings":"","what":"aorsf 0.0.6","title":"aorsf 0.0.6","text":"CRAN release: 2023-01-06 Minor fix internal tests failing run ATLAS","code":""},{"path":"https://bcjaeger.github.io/aorsf/news/index.html","id":"aorsf-005","dir":"Changelog","previous_headings":"","what":"aorsf 0.0.5","title":"aorsf 0.0.5","text":"CRAN release: 2022-12-14 orsf() longer throws errors warnings try give single predictor. note added documentation details ?orsf explains using single predictor orsf() somewhat useless. done resolve https://github.com/mlr-org/mlr3extralearners/issues/259. predict.orsf_fit now accepts pred_horizon = 0 returns sensible values. Thanks @mattwarkentin feature request. added function perform variable selection, orsf_vs(). Made variable importance consistent respect group_factors. Originally, output orsf ungrouped VI values orsf_vi grouped values. update, orsf defaults grouped values. ungrouped values can still recovered. Fixed issue orsf_pd functions output data returned original scale.","code":""},{"path":"https://bcjaeger.github.io/aorsf/news/index.html","id":"aorsf-004","dir":"Changelog","previous_headings":"","what":"aorsf 0.0.4","title":"aorsf 0.0.4","text":"CRAN release: 2022-11-07 orsf formulas now accepts Surv objects (see https://github.com/ropensci/aorsf/issues/11) Added verbose_progress input orsf, prints messages console indicating progress. Allowance missing values orsf. Mean mode imputation performed observations missing data. values can also used impute new data missing values. Centering scaling predictors now done prior growing forest.","code":""},{"path":"https://bcjaeger.github.io/aorsf/news/index.html","id":"aorsf-003","dir":"Changelog","previous_headings":"","what":"aorsf 0.0.3","title":"aorsf 0.0.3","text":"CRAN release: 2022-10-09 Included rOpenSci reviewers Christopher Jackson, Marvin N Wright, Lukas Burk DESCRIPTION reviewers. Thank ! Added clarification docs pros/cons different variable importance techniques Added regression tests aorsf versus obliqueRSF (similar) Additional support tests functions long right hand sides Updated --bag vignette appropriate custom functions. Allow status values input data general, .e., just 0 1. Allow missing values predict functions, including partial dependence.","code":""},{"path":"https://bcjaeger.github.io/aorsf/news/index.html","id":"aorsf-002","dir":"Changelog","previous_headings":"","what":"aorsf 0.0.2","title":"aorsf 0.0.2","text":"CRAN release: 2022-09-05 Modified unit tests compatibility extra checks run CRAN.","code":""},{"path":"https://bcjaeger.github.io/aorsf/news/index.html","id":"aorsf-001","dir":"Changelog","previous_headings":"","what":"aorsf 0.0.1","title":"aorsf 0.0.1","text":"CRAN release: 2022-08-23 Added orsf_control_custom(), allows users submit custom functions identifying linear combinations inputs growing oblique decision trees. Added weights input orsf, allowing users fit orsf specific data training set. Added chf mort options predict.orsf_fit(). Mortality predictions fully implemented yet - supported partial dependence --bag error estimates. features added future update.","code":""},{"path":"https://bcjaeger.github.io/aorsf/news/index.html","id":"aorsf-0009000","dir":"Changelog","previous_headings":"","what":"aorsf 0.0.0.9000","title":"aorsf 0.0.0.9000","text":"Core features implemented: fit, interpret, predict using oblique random survival forests. Vignettes + Readme covering usage core features. Website hosted GitHub pages, managed pkgdown.","code":""}] +[{"path":"https://bcjaeger.github.io/aorsf/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to aorsf","title":"Contributing to aorsf","text":"Want contribute aorsf? Great! aorsf initially stable state development, great deal active subsequent development envisioned. outline propose change aorsf. detailed info contributing , tidyverse packages, please see development contributing guide.","code":""},{"path":"https://bcjaeger.github.io/aorsf/CONTRIBUTING.html","id":"fixing-typos","dir":"","previous_headings":"","what":"Fixing typos","title":"Contributing to aorsf","text":"can fix typos, spelling mistakes, grammatical errors documentation directly using GitHub web interface, long changes made source file. generally means ’ll need edit roxygen2 comments .R, .Rd file. can find .R file generates .Rd reading comment first line.","code":""},{"path":"https://bcjaeger.github.io/aorsf/CONTRIBUTING.html","id":"bigger-changes","dir":"","previous_headings":"","what":"Bigger changes","title":"Contributing to aorsf","text":"want make bigger change, ’s good idea first file issue make sure someone team agrees ’s needed. ’ve found bug, please file issue illustrates bug minimal reprex (also help write unit test, needed).","code":""},{"path":"https://bcjaeger.github.io/aorsf/CONTRIBUTING.html","id":"pull-request-process","dir":"","previous_headings":"Bigger changes","what":"Pull request process","title":"Contributing to aorsf","text":"Fork package clone onto computer. haven’t done , recommend using usethis::create_from_github(\"ropensci/aorsf\", fork = TRUE). Install development dependencies devtools::install_dev_deps(), make sure package passes R CMD check running devtools::check(). R CMD check doesn’t pass cleanly, ’s good idea ask help continuing. Create Git branch pull request (PR). recommend using usethis::pr_init(\"brief-description--change\"). Make changes, commit git, create PR running usethis::pr_push(), following prompts browser. title PR briefly describe change. body PR contain Fixes #issue-number. user-facing changes, add bullet top NEWS.md (.e. just first header). Follow style described https://style.tidyverse.org/news.html.","code":""},{"path":"https://bcjaeger.github.io/aorsf/CONTRIBUTING.html","id":"code-style","dir":"","previous_headings":"Bigger changes","what":"Code style","title":"Contributing to aorsf","text":"New code follow tidyverse style guide. can use styler package apply styles, please don’t restyle code nothing PR. use roxygen2, Markdown syntax, documentation. use testthat unit tests. Contributions test cases included easier accept.","code":""},{"path":"https://bcjaeger.github.io/aorsf/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contributing to aorsf","text":"Please note aorsf project released Contributor Code Conduct. contributing project agree abide terms.","code":""},{"path":"https://bcjaeger.github.io/aorsf/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2022 aorsf authors (Byron C. Jaeger, Sawyer Welden, Nicholas M. Pajewski) Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://bcjaeger.github.io/aorsf/articles/aorsf.html","id":"background-orsf","dir":"Articles","previous_headings":"","what":"Background: ORSF","title":"Introduction to aorsf","text":"oblique random survival forest (ORSF) extension axis-based RSF algorithm. See orsf details ORSFs. see arXiv paper details algorithms used specifically aorsf.","code":""},{"path":"https://bcjaeger.github.io/aorsf/articles/aorsf.html","id":"accelerated-orsf","dir":"Articles","previous_headings":"","what":"Accelerated ORSF","title":"Introduction to aorsf","text":"purpose aorsf (‘’ short accelerated) provide routines fit ORSFs scale adequately large data sets. fastest algorithm available package accelerated ORSF model, default method used orsf(): may notice first input aorsf data. design choice makes easier use orsf pipes (.e., %>% |>). instance,","code":"library(aorsf) set.seed(329) orsf_fit <- orsf(data = pbc_orsf, formula = Surv(time, status) ~ . - id) orsf_fit #> ---------- Oblique random survival forest #> #> Linear combinations: Accelerated #> N observations: 276 #> N events: 111 #> N trees: 500 #> N predictors total: 17 #> N predictors per node: 5 #> Average leaves per tree: 25 #> Min observations in leaf: 5 #> Min events in leaf: 1 #> OOB stat value: 0.84 #> OOB stat type: Harrell's C-statistic #> Variable importance: anova #> #> ----------------------------------------- library(dplyr) orsf_fit <- pbc_orsf |> select(-id) |> orsf(formula = Surv(time, status) ~ .)"},{"path":"https://bcjaeger.github.io/aorsf/articles/aorsf.html","id":"interpretation","dir":"Articles","previous_headings":"","what":"Interpretation","title":"Introduction to aorsf","text":"aorsf includes several functions dedicated interpretation ORSFs, estimation partial dependence variable importance.","code":""},{"path":"https://bcjaeger.github.io/aorsf/articles/aorsf.html","id":"variable-importance","dir":"Articles","previous_headings":"Interpretation","what":"Variable importance","title":"Introduction to aorsf","text":"aorsf provides multiple ways compute variable importance. compute negation importance, ORSF multiplies coefficient variable -1 re-computes --sample (sometimes referred --bag) accuracy ORSF model. can also compute variable importance using permutation, classical approach. faster alternative permutation negation importance ANOVA importance, computes proportion times variable obtains low p-value (p < 0.01) forest grown.","code":"orsf_vi_negate(orsf_fit) #> bili copper protime stage sex #> 1.129815e-01 5.195790e-02 2.979064e-02 2.948335e-02 2.646991e-02 #> age albumin ascites ast chol #> 2.274087e-02 2.194915e-02 1.566330e-02 1.225677e-02 1.211119e-02 #> edema trt hepato spiders trig #> 9.582936e-03 7.903782e-03 6.753772e-03 6.166109e-03 5.264650e-03 #> alk.phos platelet #> 3.522158e-03 -1.435386e-05 orsf_vi_permute(orsf_fit) #> bili copper protime albumin age #> 0.0511008531 0.0240482013 0.0163342275 0.0124345455 0.0119496253 #> stage ascites ast edema chol #> 0.0118205689 0.0104882375 0.0076936637 0.0060404151 0.0049699614 #> hepato spiders sex trig alk.phos #> 0.0035818563 0.0034644598 0.0021725738 0.0018539837 0.0012967692 #> platelet trt #> -0.0003284245 -0.0009058986 orsf_vi_anova(orsf_fit) #> ascites edema bili copper albumin age protime spiders #> 0.4244652 0.2965880 0.2955043 0.2192982 0.2059560 0.2034433 0.1946442 0.1717902 #> chol stage ast hepato sex trig alk.phos platelet #> 0.1686003 0.1575789 0.1573754 0.1476569 0.1462905 0.1272040 0.1161886 0.1089885 #> trt #> 0.1086503"},{"path":"https://bcjaeger.github.io/aorsf/articles/aorsf.html","id":"partial-dependence-pd","dir":"Articles","previous_headings":"Interpretation","what":"Partial dependence (PD)","title":"Introduction to aorsf","text":"Partial dependence (PD) shows expected prediction model function single predictor multiple predictors. expectation marginalized values predictors, giving something like multivariable adjusted estimate model’s prediction. PD, see vignette","code":""},{"path":"https://bcjaeger.github.io/aorsf/articles/aorsf.html","id":"individual-conditional-expectations-ice","dir":"Articles","previous_headings":"Interpretation","what":"Individual conditional expectations (ICE)","title":"Introduction to aorsf","text":"Unlike partial dependence, shows expected prediction function one multiple predictors, individual conditional expectations (ICE) show prediction individual observation function predictor. ICE, see vignette","code":""},{"path":"https://bcjaeger.github.io/aorsf/articles/aorsf.html","id":"what-about-the-original-orsf","dir":"Articles","previous_headings":"","what":"What about the original ORSF?","title":"Introduction to aorsf","text":"original ORSF (.e., obliqueRSF) used glmnet find linear combinations inputs. aorsf allows users implement approach using orsf_control_net() function: net forests fit lot faster original ORSF function obliqueRSF. However, net forests still much slower cph ones:","code":"orsf_net <- orsf(data = pbc_orsf, formula = Surv(time, status) ~ . - id, control = orsf_control_net(), n_tree = 50) # tracking how long it takes to fit 50 glmnet trees print( t1 <- system.time( orsf(data = pbc_orsf, formula = Surv(time, status) ~ . - id, control = orsf_control_net(), n_tree = 50) ) ) #> user system elapsed #> 6.498 0.020 6.523 # and how long it takes to fit 50 cph trees print( t2 <- system.time( orsf(data = pbc_orsf, formula = Surv(time, status) ~ . - id, control = orsf_control_cph(), n_tree = 50) ) ) #> user system elapsed #> 0.068 0.000 0.067 t1['elapsed'] / t2['elapsed'] #> elapsed #> 97.35821"},{"path":"https://bcjaeger.github.io/aorsf/articles/aorsf.html","id":"aorsf-and-other-machine-learning-software","dir":"Articles","previous_headings":"","what":"aorsf and other machine learning software","title":"Introduction to aorsf","text":"unique feature aorsf fast algorithms fit ORSF ensembles. RLT obliqueRSF fit oblique random survival forests, aorsf faster. ranger randomForestSRC fit survival forests, neither package supports oblique splitting. obliqueRF fits oblique random forests classification regression, survival. PPforest fits oblique random forests classification survival. Note: default prediction behavior aorsf models produce predicted risk specific prediction horizon, default ranger randomForestSRC. think change future, computing time independent predictions aorsf helpful.","code":""},{"path":"https://bcjaeger.github.io/aorsf/articles/oobag.html","id":"out-of-bag-data","dir":"Articles","previous_headings":"","what":"Out-of-bag data","title":"Out-of-bag predictions and evaluation","text":"random forests, tree grown bootstrapped version training set. bootstrap samples selected replacement, bootstrapped training set contains two-thirds instances original training set. ‘--bag’ data instances bootstrapped training set.","code":""},{"path":"https://bcjaeger.github.io/aorsf/articles/oobag.html","id":"out-of-bag-predictions-and-error","dir":"Articles","previous_headings":"","what":"Out-of-bag predictions and error","title":"Out-of-bag predictions and evaluation","text":"tree random forest can make predictions --bag data, --bag predictions can aggregated make ensemble --bag prediction. Since --bag data used grow tree, accuracy ensemble --bag predictions approximate generalization error random forest. --bag prediction error plays central role routines estimate variable importance, e.g. negation importance. Let’s fit oblique random survival forest plot distribution ensemble --bag predictions. surprisingly, survival predictions 0 1. Next, let’s check --bag accuracy fit: --bag estimate Harrell’s C-statistic (default method evaluate --bag predictions) 0.8405646.","code":"fit <- orsf(data = pbc_orsf, formula = Surv(time, status) ~ . - id, oobag_pred_type = 'surv', oobag_pred_horizon = 2000) hist(fit$pred_oobag, main = 'Ensemble out-of-bag survival predictions at t=3,500') # what function is used to evaluate out-of-bag predictions? fit$eval_oobag$stat_type #> [1] \"Harrell's C-statistic\" # what is the output from this function? fit$eval_oobag$stat_values #> [,1] #> [1,] 0.8405646"},{"path":"https://bcjaeger.github.io/aorsf/articles/oobag.html","id":"monitoring-out-of-bag-error","dir":"Articles","previous_headings":"","what":"Monitoring out-of-bag error","title":"Out-of-bag predictions and evaluation","text":"--bag data set contains one-third training set, --bag error estimate usually converges stable value trees added forest. want monitor convergence --bag error oblique random survival forest, can set oobag_eval_every compute --bag error every oobag_eval_every tree. example, let’s compute --bag error fitting tree forest 50 trees: general, least 500 trees recommended random forest fit. ’re just using 50 case better illustration --bag error curve. Also, helps make run-times low whenever need re-compile package vignettes.","code":"fit <- orsf(data = pbc_orsf, formula = Surv(time, status) ~ . - id, n_tree = 50, oobag_pred_type = 'surv', oobag_pred_horizon = 2000, oobag_eval_every = 1) plot( x = seq(1, 50, by = 1), y = fit$eval_oobag$stat_values, main = 'Out-of-bag C-statistic computed after each new tree is grown.', xlab = 'Number of trees grown', ylab = fit$eval_oobag$stat_type )"},{"path":"https://bcjaeger.github.io/aorsf/articles/oobag.html","id":"user-supplied-out-of-bag-evaluation-functions","dir":"Articles","previous_headings":"","what":"User-supplied out-of-bag evaluation functions","title":"Out-of-bag predictions and evaluation","text":"cases, may want control --bag error estimated. example, let’s use Brier score SurvMetrics package: two ways apply function compute --bag error. First, can apply function --bag survival predictions stored ‘aorsf’ objects, e.g: Second, can pass function orsf(), used place Harrell’s C-statistic: can also compute time-dependent C-statistic instead Harrell’s C-statistic (default oob function):","code":"oobag_fun_brier <- function(y_mat, w_vec, s_vec){ # output is numeric vector of length 1 as.numeric( SurvMetrics::Brier( object = Surv(time = y_mat[, 1], event = y_mat[, 2]), pre_sp = s_vec, # t_star in Brier() should match oob_pred_horizon in orsf() t_star = 2000 ) ) } oobag_fun_brier(y_mat = pbc_orsf[,c('time', 'status')], s_vec = fit$pred_oobag) #> [1] 0.113018 fit <- orsf(data = pbc_orsf, formula = Surv(time, status) ~ . - id, n_tree = 50, oobag_pred_horizon = 2000, oobag_fun = oobag_fun_brier, oobag_eval_every = 1) plot( x = seq(1, 50, by = 1), y = fit$eval_oobag$stat_values, main = 'Out-of-bag error computed after each new tree is grown.', sub = 'For the Brier score, lower values indicate more accurate predictions', xlab = 'Number of trees grown', ylab = \"Brier score\" ) oobag_fun_tdep_cstat <- function(y_mat, w_vec, s_vec){ as.numeric( SurvMetrics::Cindex( object = Surv(time = y_mat[, 1], event = y_mat[, 2]), predicted = s_vec, t_star = 2000 ) ) } fit <- orsf(data = pbc_orsf, formula = Surv(time, status) ~ . - id, n_tree = 50, oobag_pred_horizon = 2000, oobag_fun = oobag_fun_tdep_cstat, oobag_eval_every = 1) plot( x = seq(50), y = fit$eval_oobag$stat_values, main = 'Out-of-bag time-dependent AUC\\ncomputed after each new tree is grown.', xlab = 'Number of trees grown', ylab = \"AUC at t = 2,000\" )"},{"path":"https://bcjaeger.github.io/aorsf/articles/oobag.html","id":"specific-instructions-on-user-supplied-functions","dir":"Articles","previous_headings":"User-supplied out-of-bag evaluation functions","what":"Specific instructions on user-supplied functions","title":"Out-of-bag predictions and evaluation","text":"User-supplied functions must: exactly three arguments named y_mat, w_vec, s_vec. return numeric output length 1 either conditions true, error occur. simple test make sure user-supplied function work aorsf package :","code":"# Helper code to make sure your oobag_fun function will work with aorsf # time and status values test_time <- seq(from = 1, to = 5, length.out = 100) test_status <- rep(c(0,1), each = 50) # y-matrix is presumed to contain time and status (with column names) y_mat <- cbind(time = test_time, status = test_status) # s_vec is presumed to be a vector of survival probabilities s_vec <- seq(0.9, 0.1, length.out = 100) # see 1 in the checklist above names(formals(oobag_fun_tdep_cstat)) == c(\"y_mat\", \"w_vec\", \"s_vec\") #> [1] TRUE TRUE TRUE test_output <- oobag_fun_tdep_cstat(y_mat = y_mat, w_vec = w_vec, s_vec = s_vec) # test output should be numeric is.numeric(test_output) #> [1] TRUE # test_output should be a numeric value of length 1 length(test_output) == 1 #> [1] TRUE"},{"path":"https://bcjaeger.github.io/aorsf/articles/oobag.html","id":"user-supplied-functions-for-negation-importance-","dir":"Articles","previous_headings":"","what":"User-supplied functions for negation importance.","title":"Out-of-bag predictions and evaluation","text":"Negation importance based --bag error, course may curious negation importance computed using different statistics. workflow exactly example , except two things: specify importance = 'negate' fit model. want use modified version C-stat, specifically 1 - C-stat, aorsf computes variable importance. Also, speed computations, going monitor --bag error .","code":"oobag_fun_tdep_cstat_inverse <- function(y_mat, w_vec, s_vec){ 1 - oobag_fun_tdep_cstat(y_mat, w_vec, s_vec) } fit_tdep_cstat <- orsf(data = pbc_orsf, formula = Surv(time, status) ~ . - id, n_tree = 100, oobag_pred_horizon = 2000, oobag_fun = oobag_fun_tdep_cstat_inverse, importance = 'negate') fit_tdep_cstat$importance #> bili copper stage sex albumin protime age #> 0.12277976 0.05474438 0.03624949 0.03600352 0.02799870 0.02613815 0.02258938 #> ascites ast chol edema spiders hepato trig #> 0.01396824 0.01370726 0.01291091 0.01011906 0.00679223 0.00659164 0.00615851 #> platelet trt alk.phos #> 0.00490489 0.00373202 0.00066513"},{"path":"https://bcjaeger.github.io/aorsf/articles/oobag.html","id":"notes","dir":"Articles","previous_headings":"","what":"Notes","title":"Out-of-bag predictions and evaluation","text":"evaluating --bag error: oobag_pred_horizon input orsf() determines prediction horizon --bag predictions. prediction horizon needs specified evaluate prediction accuracy cases, examples . sure check case using functions, , sure oobag_pred_horizon matches prediction horizon used custom function. functions expect predicted risk (.e., 1 - predicted survival), others expect predicted survival. cases, also able use function whatsoever compute --bag prediction error estimating negation permutation importance, assuming passes tests . Unfortunately, exception riskRegression::Score(), one favorites. experimented riskRegression::Score found work try run C++. sure case.","code":""},{"path":"https://bcjaeger.github.io/aorsf/articles/pd.html","id":"partial-dependence-pd","dir":"Articles","previous_headings":"","what":"Partial dependence (PD)","title":"PD and ICE curves with ORSF","text":"Partial dependence (PD) shows expected prediction model function single predictor multiple predictors. expectation marginalized values predictors, giving something like multivariable adjusted estimate model’s prediction. Begin fitting ORSF ensemble. Set prediction horizon 5 years fit ensemble aorsf function pass ensemble assume want compute predictions 5 years.","code":"library(aorsf) pred_horizon <- 365.25 * 5 set.seed(329730) index_train <- sample(nrow(pbc_orsf), 150) pbc_orsf_train <- pbc_orsf[index_train, ] pbc_orsf_test <- pbc_orsf[-index_train, ] fit <- orsf(data = pbc_orsf_train, formula = Surv(time, status) ~ . - id, oobag_pred_horizon = pred_horizon) fit #> ---------- Oblique random survival forest #> #> Linear combinations: Accelerated #> N observations: 150 #> N events: 52 #> N trees: 500 #> N predictors total: 17 #> N predictors per node: 5 #> Average leaves per tree: 12 #> Min observations in leaf: 5 #> Min events in leaf: 1 #> OOB stat value: 0.83 #> OOB stat type: Harrell's C-statistic #> Variable importance: anova #> #> -----------------------------------------"},{"path":"https://bcjaeger.github.io/aorsf/articles/pd.html","id":"three-ways-to-compute-pd","dir":"Articles","previous_headings":"","what":"Three ways to compute PD","title":"PD and ICE curves with ORSF","text":"can compute PD three ways aorsf: using -bag predictions training data using --bag predictions training data using predictions new set data -bag PD indicates relationships model learned training. helpful goal interpret model. --bag PD indicates relationships model learned training using --bag data simulates application model new data. want test model’s reliability fairness new data don’t access large testing set. new data PD shows model predicts outcomes observations seen. helpful want test model’s reliability fairness. Let’s re-fit ORSF available data proceeding next sections.","code":"pd_inb <- orsf_pd_inb(fit, pred_spec = list(bili = 1:5)) pd_inb #> pred_horizon bili mean lwr medn upr #> 1: 1826.25 1 0.2154847 0.02028479 0.09620362 0.7999464 #> 2: 1826.25 2 0.2580146 0.03766695 0.15454947 0.8215570 #> 3: 1826.25 3 0.3001896 0.06432488 0.20728050 0.8429332 #> 4: 1826.25 4 0.3394211 0.08427149 0.25388024 0.8601380 #> 5: 1826.25 5 0.3703022 0.10680098 0.28301801 0.8696998 pd_oob <- orsf_pd_oob(fit, pred_spec = list(bili = 1:5)) pd_oob #> pred_horizon bili mean lwr medn upr #> 1: 1826.25 1 0.2151526 0.01835000 0.0961149 0.7980629 #> 2: 1826.25 2 0.2572420 0.03685020 0.1444598 0.8181867 #> 3: 1826.25 3 0.2990080 0.05900059 0.2069944 0.8335823 #> 4: 1826.25 4 0.3388657 0.07887323 0.2434497 0.8486574 #> 5: 1826.25 5 0.3701697 0.10614495 0.2805791 0.8523756 pd_test <- orsf_pd_new(fit, new_data = pbc_orsf_test, pred_spec = list(bili = 1:5)) pd_test #> pred_horizon bili mean lwr medn upr #> 1: 1826.25 1 0.2543006 0.02901386 0.1943949 0.8140307 #> 2: 1826.25 2 0.2956375 0.05072616 0.2473845 0.8314078 #> 3: 1826.25 3 0.3389084 0.07453896 0.3032327 0.8485016 #> 4: 1826.25 4 0.3800621 0.10565022 0.3516712 0.8588451 #> 5: 1826.25 5 0.4125041 0.12292465 0.3918400 0.8694518 set.seed(329730) fit <- orsf(pbc_orsf, Surv(time, status) ~ . -id, oobag_pred_horizon = pred_horizon)"},{"path":"https://bcjaeger.github.io/aorsf/articles/pd.html","id":"one-variable-one-horizon","dir":"Articles","previous_headings":"","what":"One variable, one horizon","title":"PD and ICE curves with ORSF","text":"Computing PD single variable straightforward: output shows expected predicted mortality risk men substantially higher women 5 years baseline.","code":"pd_sex <- orsf_pd_oob(fit, pred_spec = list(sex = c(\"m\", \"f\"))) pd_sex #> pred_horizon sex mean lwr medn upr #> 1: 1826.25 m 0.3564912 0.03712878 0.2369997 0.9398747 #> 2: 1826.25 f 0.3035109 0.01053790 0.1568024 0.9545274"},{"path":"https://bcjaeger.github.io/aorsf/articles/pd.html","id":"one-variable-moving-horizon","dir":"Articles","previous_headings":"","what":"One variable, moving horizon","title":"PD and ICE curves with ORSF","text":"effect predictor varies time? PD can show . inspection, can see males higher risk females difference risk grows time. can also seen viewing ratio expected risk time:","code":"pd_sex_tv <- orsf_pd_oob(fit, pred_spec = list(sex = c(\"m\", \"f\")), pred_horizon = seq(365, 365*5)) ggplot(pd_sex_tv, aes(x = pred_horizon, y = mean, color = sex)) + geom_line() + labs(x = 'Time since baseline', y = 'Expected risk') library(data.table) ratio_tv <- pd_sex_tv[ , .(ratio = mean[sex == 'm'] / mean[sex == 'f']), by = pred_horizon ] ggplot(ratio_tv, aes(x = pred_horizon, y = ratio)) + geom_line(color = 'grey') + geom_smooth(color = 'black', se = FALSE) + labs(x = 'time since baseline', y = 'ratio in expected risk for males versus females') #> `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = \"cs\")'"},{"path":"https://bcjaeger.github.io/aorsf/articles/pd.html","id":"multiple-variables-marginally","dir":"Articles","previous_headings":"","what":"Multiple variables, marginally","title":"PD and ICE curves with ORSF","text":"want compute PD marginally multiple variables, just list variable values pred_spec specify expand_grid = FALSE. Now tedious wanted variables? bet. ’s made function . bonus, printed output sorted least important variables. ’s easy enough turn ‘summary’ object data.table downstream plotting tables.","code":"pd_two_vars <- orsf_pd_oob(fit, pred_spec = list(sex = c(\"m\", \"f\"), bili = 1:5), expand_grid = FALSE) pd_two_vars #> pred_horizon variable value level mean lwr medn upr #> 1: 1826.25 sex NA m 0.3564912 0.03712878 0.2369997 0.9398747 #> 2: 1826.25 sex NA f 0.3035109 0.01053790 0.1568024 0.9545274 #> 3: 1826.25 bili 1 <NA> 0.2461638 0.01583046 0.1295294 0.8963509 #> 4: 1826.25 bili 2 <NA> 0.3023356 0.03962917 0.2026023 0.9165164 #> 5: 1826.25 bili 3 <NA> 0.3519626 0.06060907 0.2635266 0.9220564 #> 6: 1826.25 bili 4 <NA> 0.3947579 0.08420548 0.3188143 0.9267172 #> 7: 1826.25 bili 5 <NA> 0.4293114 0.10880143 0.3618061 0.9255556 pd_smry <- orsf_summarize_uni(fit) pd_smry #> #> -- bili (VI Rank: 1) ------------------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 0.80 0.2384834 0.1161447 0.05083804 0.3819801 #> 1.4 0.2650961 0.1520876 0.07038703 0.4319252 #> 3.5 0.3757328 0.2962150 0.16422471 0.5693036 #> #> -- copper (VI Rank: 2) ----------------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 43 0.2674680 0.1379648 0.05160351 0.4454352 #> 74 0.2916843 0.1599240 0.06762896 0.4988722 #> 129 0.3468187 0.2274629 0.11003062 0.5575685 #> #> -- sex (VI Rank: 3) -------------------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> m 0.3564912 0.2369997 0.10787696 0.5872914 #> f 0.3035109 0.1568024 0.05434944 0.5526196 #> #> -- stage (VI Rank: 4) ------------------------------------ #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 1 0.5847147 0.52941 0.3996593 0.7568616 #> 2 0.5847147 0.52941 0.3996593 0.7568616 #> 3 0.5847147 0.52941 0.3996593 0.7568616 #> 4 0.5847147 0.52941 0.3996593 0.7568616 #> #> -- age (VI Rank: 5) -------------------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 42 0.2769998 0.1475089 0.04641807 0.4656813 #> 50 0.3105800 0.1962849 0.05238234 0.5260957 #> 57 0.3458171 0.2416166 0.08073215 0.5838783 #> #> -- protime (VI Rank: 6) ---------------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 10 0.2868796 0.1565407 0.05348739 0.5082739 #> 11 0.3049211 0.1630376 0.05660416 0.5398259 #> 11 0.3279135 0.1941268 0.07126535 0.5817064 #> #> -- albumin (VI Rank: 7) ---------------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 3.3 0.3268318 0.1898688 0.06087156 0.6078501 #> 3.5 0.3040046 0.1564467 0.05753060 0.5400452 #> 3.8 0.2861643 0.1576060 0.05303617 0.5026478 #> #> -- ascites (VI Rank: 8) ---------------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 0 0.3028570 0.1542828 0.05434944 0.5393245 #> 1 0.4525678 0.3667405 0.24229217 0.6413067 #> #> -- chol (VI Rank: 9) ------------------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 250 0.2931503 0.1529514 0.04780508 0.5046722 #> 310 0.3029789 0.1662991 0.05563008 0.5144736 #> 401 0.3256828 0.1983822 0.07294784 0.5492762 #> #> -- ast (VI Rank: 10) ------------------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 82 0.2903896 0.1490766 0.05113070 0.5159897 #> 117 0.3076024 0.1648002 0.05698760 0.5501132 #> 153 0.3307164 0.1800020 0.07086705 0.6059466 #> #> -- edema (VI Rank: 11) ----------------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 0 0.2968877 0.1534589 0.0539005 0.5366297 #> 0.5 0.3639845 0.2581248 0.1059203 0.6140513 #> 1 0.4571985 0.3727463 0.2549830 0.6499621 #> #> -- spiders (VI Rank: 12) --------------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 0 0.2984039 0.1528105 0.05324006 0.5321606 #> 1 0.3438490 0.2238338 0.09693236 0.5660642 #> #> -- hepato (VI Rank: 13) ---------------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 0 0.2924451 0.1513041 0.05269621 0.5291405 #> 1 0.3274109 0.1826162 0.07423325 0.5488156 #> #> -- trt (VI Rank: 14) ------------------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> d_penicill_main 0.3146055 0.1808374 0.06225388 0.5581896 #> placebo 0.3092247 0.1616993 0.05455801 0.5462009 #> #> -- trig (VI Rank: 15) ------------------------------------ #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 85 0.3020625 0.1594529 0.04947666 0.5368518 #> 108 0.3094585 0.1642559 0.05138019 0.5381577 #> 151 0.3236035 0.1919256 0.06579255 0.5484254 #> #> -- alk.phos (VI Rank: 16) -------------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 922 0.3125063 0.1746096 0.05547749 0.5655240 #> 1278 0.3143534 0.1730928 0.06012625 0.5716067 #> 2068 0.3175794 0.1719252 0.05909212 0.5968980 #> #> -- platelet (VI Rank: 17) -------------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 200 0.3178789 0.1797536 0.05520746 0.5855790 #> 257 0.3120217 0.1700296 0.05532263 0.5736814 #> 318 0.3087421 0.1739535 0.05590358 0.5649877 #> #> Predicted risk at time t = 1826.25 for top 17 predictors head(as.data.table(pd_smry)) #> variable importance Value Mean Median 25th % 75th % #> 1: bili 0.11319592 0.80 0.2384834 0.1161447 0.05083804 0.3819801 #> 2: bili 0.11319592 1.4 0.2650961 0.1520876 0.07038703 0.4319252 #> 3: bili 0.11319592 3.5 0.3757328 0.2962150 0.16422471 0.5693036 #> 4: copper 0.04949475 43 0.2674680 0.1379648 0.05160351 0.4454352 #> 5: copper 0.04949475 74 0.2916843 0.1599240 0.06762896 0.4988722 #> 6: copper 0.04949475 129 0.3468187 0.2274629 0.11003062 0.5575685 #> pred_horizon level #> 1: 1826.25 <NA> #> 2: 1826.25 <NA> #> 3: 1826.25 <NA> #> 4: 1826.25 <NA> #> 5: 1826.25 <NA> #> 6: 1826.25 <NA>"},{"path":"https://bcjaeger.github.io/aorsf/articles/pd.html","id":"multiple-variables-jointly","dir":"Articles","previous_headings":"","what":"Multiple variables, jointly","title":"PD and ICE curves with ORSF","text":"PD can show expected value model’s predictions function specific predictor, function multiple predictors. instance, can estimate predicted risk joint function bili, edema, trt: inspection, model’s predictions indicate slightly lower risk placebo group, seem change much different values bili edema. clear increase predicted risk higher levels edema higher levels bili slope predicted risk function bili appears highest among patients edema 0.5. effect bili modified edema 0.5? quick sanity check coxph suggests .","code":"pred_spec = list(bili = seq(1, 5, length.out = 20), edema = levels(pbc_orsf_train$edema), trt = levels(pbc_orsf$trt)) pd_bili_edema <- orsf_pd_oob(fit, pred_spec) library(ggplot2) ggplot(pd_bili_edema, aes(x = bili, y = medn, col = trt, linetype = edema)) + geom_line() + labs(y = 'Expected predicted risk') library(survival) pbc_orsf$edema_05 <- ifelse(pbc_orsf$edema == '0.5', 'yes', 'no') fit_cph <- coxph(Surv(time,status) ~ edema_05 * bili, data = pbc_orsf) anova(fit_cph) #> Analysis of Deviance Table #> Cox model: response is Surv(time, status) #> Terms added sequentially (first to last) #> #> loglik Chisq Df Pr(>|Chi|) #> NULL -550.19 #> edema_05 -546.83 6.7248 1 0.009508 ** #> bili -513.59 66.4689 1 3.555e-16 *** #> edema_05:bili -510.54 6.1112 1 0.013433 * #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1"},{"path":"https://bcjaeger.github.io/aorsf/articles/pd.html","id":"individual-conditional-expectations-ice","dir":"Articles","previous_headings":"","what":"Individual conditional expectations (ICE)","title":"PD and ICE curves with ORSF","text":"Unlike partial dependence, shows expected prediction function one multiple predictors, individual conditional expectations (ICE) show prediction individual observation function predictor. Just like PD, can compute ICE using -bag, --bag, testing data, principles apply. ’ll use --bag estimates .","code":""},{"path":"https://bcjaeger.github.io/aorsf/articles/pd.html","id":"visualizing-ice-curves","dir":"Articles","previous_headings":"","what":"Visualizing ICE curves","title":"PD and ICE curves with ORSF","text":"Inspecting ICE curves observation can help identify whether heterogeneity model’s predictions. .e., effect variable follow pattern data, groups variable impacts risk differently? going turn boundary checking orsf_ice_oob setting boundary_checks = FALSE, allow generate ICE curves go beyond 90th percentile bili. id_variable identifier current value variable(s) data. redundant one variable, helpful multiple variables. id_row identifier observation original data. used group observation’s predictions together plots. plots, helpful scale ICE data. subtract initial value predicted risk (.e., bili = 1) observation’s conditional expectation values. , Every curve start 0 plot shows change predicted risk function bili. Now can visualize curves. inspection figure, individual slopes cluster around overall trend - Good! small number individual slopes appear flat. may helpful investigate .","code":"pred_spec <- list(bili = seq(1, 10, length.out = 25)) ice_oob <- orsf_ice_oob(fit, pred_spec, boundary_checks = FALSE) ice_oob #> id_variable id_row pred_horizon bili pred #> 1: 1 1 1826.25 1 0.9194969 #> 2: 1 2 1826.25 1 0.1136944 #> 3: 1 3 1826.25 1 0.7413338 #> 4: 1 4 1826.25 1 0.3671091 #> 5: 1 5 1826.25 1 0.1439086 #> --- #> 6896: 25 272 1826.25 10 0.3218517 #> 6897: 25 273 1826.25 10 0.4362345 #> 6898: 25 274 1826.25 10 0.4962449 #> 6899: 25 275 1826.25 10 0.3131265 #> 6900: 25 276 1826.25 10 0.5433389 ice_oob[, pred_subtract := rep(pred[id_variable==1], times=25)] ice_oob[, pred := pred - pred_subtract] library(ggplot2) ggplot(ice_oob, aes(x = bili, y = pred, group = id_row)) + geom_line(alpha = 0.15) + labs(y = 'Change in predicted risk') + geom_smooth(se = FALSE, aes(group = 1)) #> `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = \"cs\")'"},{"path":"https://bcjaeger.github.io/aorsf/articles/pd.html","id":"limitations-of-pd","dir":"Articles","previous_headings":"","what":"Limitations of PD","title":"PD and ICE curves with ORSF","text":"Partial dependence number known limitations assumptions users aware (see Hooker, 2021). particular, partial dependence less intuitive >2 predictors examined jointly, assumed feature(s) partial dependence computed correlated features (likely true many cases). Accumulated local effect plots can used (see ) case feature independence valid assumption.","code":""},{"path":"https://bcjaeger.github.io/aorsf/articles/pd.html","id":"references","dir":"Articles","previous_headings":"","what":"References","title":"PD and ICE curves with ORSF","text":"Giles Hooker, Lucas Mentch, Siyu Zhou. Unrestricted Permutation forces Extrapolation: Variable Importance Requires least One Model, Free Variable Importance. arXiv e-prints 2021 Oct; arXiv-1905. URL: https://doi.org/10.48550/arXiv.1905.03151","code":""},{"path":"https://bcjaeger.github.io/aorsf/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Byron Jaeger. Author, maintainer. Nicholas Pajewski. Contributor. Sawyer Welden. Contributor. Christopher Jackson. Reviewer. Marvin Wright. Reviewer. Lukas Burk. Reviewer.","code":""},{"path":"https://bcjaeger.github.io/aorsf/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Jaeger et al. (2022). aorsf: R package supervised learning using oblique random survival forest. Journal Open Source Software, 7(77), 4705. https://doi.org/10.21105/joss.04705. Jaeger BC, Welden S, Lenoir K, Speiser JL, Segar MW, Pandey , Pajewski NM. Accelerated interpretable oblique random survival forests. arXiv e-prints. 2022 Aug 3:arXiv-2208. Jaeger BC, Long DL, Long DM, Sims M, Szychowski JM, Min YI, Mcclure LA, Howard G, Simon N. Oblique Random Survival Forests. Annals Applied Statistics. 13(3): 1847-1883. URL https://doi.org/10.1214/19-AOAS1261 DOI: 10.1214/19-AOAS1261","code":"@Article{, title = {aorsf: An R package for supervised learning using the oblique random survival forest}, author = {Byron C. Jaeger and Sawyer Welden and Kristin Lenoir and Nicholas M. Pajewski}, journal = {Journal of Open Source Software}, year = {2022}, volume = {7}, number = {77}, pages = {4705}, url = {https://doi.org/10.21105/joss.04705}, } @Article{, title = {Accelerated and interpretable oblique random survival forests}, author = {Byron C. Jaeger and Sawyer Welden and Kristin Lenoir and Jaime L. Speiser and Matthew W. Segar and Ambarish Pandey and Nicholas M. Pajewski}, journal = {arXiv}, year = {2022}, url = {https://arxiv.org/abs/2208.01129}, } @Article{, title = {Oblique Random Survival Forests}, author = {Byron C. Jaeger and D. Leann Long and Dustin M. Long and Mario Sims and Jeff M. Szychowski and Yuan-I Min and Leslie A. Mcclure and George Howard and Noah Simon}, journal = {Annals of Applied Statistics}, year = {2019}, volume = {13}, number = {3}, pages = {1847--1883}, url = {https://doi.org/10.1214/19-AOAS1261}, }"},{"path":"https://bcjaeger.github.io/aorsf/index.html","id":"aorsf-","dir":"","previous_headings":"","what":"Accelerated Oblique Random Survival Forests","title":"Accelerated Oblique Random Survival Forests","text":"Fit, interpret, make predictions oblique random survival forests (ORSFs).","code":""},{"path":"https://bcjaeger.github.io/aorsf/index.html","id":"why-aorsf","dir":"","previous_headings":"","what":"Why aorsf?","title":"Accelerated Oblique Random Survival Forests","text":"Hundreds times faster obliqueRSF.1 Accurate predictions censored outcomes.2 Negation importance, novel technique estimate variable importance ORSFs.2 Intuitive API formula based interface. Extensive input checks informative error messages.","code":""},{"path":"https://bcjaeger.github.io/aorsf/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Accelerated Oblique Random Survival Forests","text":"can install aorsf CRAN using can install development version aorsf GitHub :","code":"install.packages(\"aorsf\") # install.packages(\"remotes\") remotes::install_github(\"ropensci/aorsf\")"},{"path":"https://bcjaeger.github.io/aorsf/index.html","id":"what-is-an-oblique-decision-tree","dir":"","previous_headings":"","what":"What is an oblique decision tree?","title":"Accelerated Oblique Random Survival Forests","text":"Decision trees developed splitting set training data two new subsets, goal similarity within new subsets . splitting process repeated resulting subsets data stopping criterion met. new subsets data formed based single predictor, decision tree said axis-based splits data appear perpendicular axis predictor. linear combinations variables used instead single variable, tree oblique splits data neither parallel right angle axis. Figure: Decision trees classification axis-based splitting (left) oblique splitting (right). Cases orange squares; controls purple circles. trees partition predictor space defined variables X1 X2, oblique splits better job separating two classes.","code":""},{"path":"https://bcjaeger.github.io/aorsf/index.html","id":"examples","dir":"","previous_headings":"","what":"Examples","title":"Accelerated Oblique Random Survival Forests","text":"orsf() function can fit several types ORSF ensembles. personal favorite accelerated ORSF great combination prediction accuracy computational efficiency (see arXiv paper).2","code":"library(aorsf) set.seed(329730) index_train <- sample(nrow(pbc_orsf), 150) pbc_orsf_train <- pbc_orsf[index_train, ] pbc_orsf_test <- pbc_orsf[-index_train, ] fit <- orsf(data = pbc_orsf_train, formula = Surv(time, status) ~ . - id, oobag_pred_horizon = 365.25 * 5)"},{"path":"https://bcjaeger.github.io/aorsf/index.html","id":"inspect","dir":"","previous_headings":"Examples","what":"Inspect","title":"Accelerated Oblique Random Survival Forests","text":"Printing output orsf() give information descriptive statistics ensemble. See print.orsf_fit description line printed output. See orsf examples details controlling ORSF ensemble fits using prediction modeling workflows.","code":"fit #> ---------- Oblique random survival forest #> #> Linear combinations: Accelerated #> N observations: 150 #> N events: 52 #> N trees: 500 #> N predictors total: 17 #> N predictors per node: 5 #> Average leaves per tree: 12 #> Min observations in leaf: 5 #> Min events in leaf: 1 #> OOB stat value: 0.83 #> OOB stat type: Harrell's C-statistic #> Variable importance: anova #> #> -----------------------------------------"},{"path":"https://bcjaeger.github.io/aorsf/index.html","id":"variable-importance","dir":"","previous_headings":"Examples","what":"Variable importance","title":"Accelerated Oblique Random Survival Forests","text":"importance individual variables can estimated three ways using aorsf: negation2: variable assessed separately multiplying variable’s coefficients -1 determining much model’s performance changes. worse model’s performance negating coefficients given variable, important variable. technique promising b/c require permutation emphasizes variables larger coefficients linear combinations, also relatively new hasn’t studied much permutation importance. See Jaeger, 2022 details technique. permutation: variable assessed separately randomly permuting variable’s values determining much model’s performance changes. worse model’s performance permuting values given variable, important variable. technique flexible, intuitive, frequently used. also several known limitations analysis variance (ANOVA)3: p-value computed coefficient linear combination variables decision tree. Importance individual predictor variable proportion times p-value coefficient < 0.01. technique efficient computationally, may effective permutation negation terms selecting signal noise variables. See Menze, 2011 details technique. can supply R function estimate --bag error using negation permutation importance. feature experimental may changed future (see oob vignette)","code":"orsf_vi_negate(fit) #> bili age sex ast ascites #> 0.0959635932 0.0162247725 0.0136525524 0.0085081124 0.0059358924 #> edema stage copper hepato chol #> 0.0051286110 0.0019786308 0.0015829046 0.0007914523 -0.0003957262 #> alk.phos albumin spiders trt platelet #> -0.0021764939 -0.0023743569 -0.0043529877 -0.0045508508 -0.0059358924 orsf_vi_permute(fit) #> bili ascites sex age edema #> 0.0096952909 0.0073209339 0.0067273447 0.0065294816 0.0037989711 #> albumin stage protime hepato chol #> 0.0031658093 0.0029679462 0.0023743569 0.0019786308 0.0007914523 #> ast spiders copper trt trig #> 0.0003957262 -0.0019786308 -0.0027700831 -0.0049465770 -0.0055401662 orsf_vi_anova(fit) #> ascites bili edema sex age copper stage #> 0.35231788 0.33216374 0.31401592 0.22045995 0.19044776 0.18155620 0.16907605 #> ast hepato albumin chol trig protime spiders #> 0.14183124 0.13736655 0.12611012 0.11461988 0.10847044 0.10697115 0.08802817 #> alk.phos platelet trt #> 0.07943094 0.06150342 0.04411765"},{"path":"https://bcjaeger.github.io/aorsf/index.html","id":"partial-dependence-pd","dir":"","previous_headings":"Examples","what":"Partial dependence (PD)","title":"Accelerated Oblique Random Survival Forests","text":"Partial dependence (PD) shows expected prediction model function single predictor multiple predictors. expectation marginalized values predictors, giving something like multivariable adjusted estimate model’s prediction. PD, see vignette","code":""},{"path":"https://bcjaeger.github.io/aorsf/index.html","id":"individual-conditional-expectations-ice","dir":"","previous_headings":"Examples","what":"Individual conditional expectations (ICE)","title":"Accelerated Oblique Random Survival Forests","text":"Unlike partial dependence, shows expected prediction function one multiple predictors, individual conditional expectations (ICE) show prediction individual observation function predictor. ICE, see vignette","code":""},{"path":"https://bcjaeger.github.io/aorsf/index.html","id":"comparison-to-existing-software","dir":"","previous_headings":"","what":"Comparison to existing software","title":"Accelerated Oblique Random Survival Forests","text":"Comparisons aorsf existing software presented arXiv paper. paper describes aorsf detail summary procedures used tree fitting algorithm runs general benchmark comparing aorsf obliqueRSF several learners reports prediction accuracy computational efficiency learners. runs simulation study comparing variable importance techniques ORSFs, axis based RSFs, boosted trees. reports probability variable importance technique rank relevant variable higher importance irrelevant variable. hands-comparison aorsf R packages provided orsf examples","code":""},{"path":"https://bcjaeger.github.io/aorsf/index.html","id":"references","dir":"","previous_headings":"","what":"References","title":"Accelerated Oblique Random Survival Forests","text":"Jaeger BC, Long DL, Long DM, Sims M, Szychowski JM, Min YI, Mcclure LA, Howard G, Simon N. Oblique random survival forests. Annals applied statistics 2019 Sep; 13(3):1847-83. DOI: 10.1214/19-AOAS1261 Jaeger BC, Welden S, Lenoir K, Speiser JL, Segar MW, Pandey , Pajewski NM. Accelerated interpretable oblique random survival forests. arXiv e-prints 2022 Aug; arXiv-2208. URL: https://arxiv.org/abs/2208.01129 Menze BH, Kelm BM, Splitthoff DN, Koethe U, Hamprecht FA. oblique random forests. Joint European Conference Machine Learning Knowledge Discovery Databases 2011 Sep 4; pp. 453-469. DOI: 10.1007/978-3-642-23783-6_29","code":""},{"path":"https://bcjaeger.github.io/aorsf/index.html","id":"funding","dir":"","previous_headings":"","what":"Funding","title":"Accelerated Oblique Random Survival Forests","text":"developers aorsf receive financial support Center Biomedical Informatics, Wake Forest University School Medicine. also receive support National Center Advancing Translational Sciences National Institutes Health Award Number UL1TR001420. content solely responsibility authors necessarily represent official views National Institutes Health.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/aorsf-package.html","id":null,"dir":"Reference","previous_headings":"","what":"aorsf: Accelerated Oblique Random Survival Forests — aorsf-package","title":"aorsf: Accelerated Oblique Random Survival Forests — aorsf-package","text":"Fit, interpret, make predictions oblique random survival forests. Oblique decision trees notoriously slow compared axis based counterparts, 'aorsf' runs fast faster axis-based decision tree algorithms right-censored time--event outcomes. Methods accelerate interpret oblique random survival forest described Jaeger et al., (2022) arXiv:2208.01129.","code":""},{"path":[]},{"path":"https://bcjaeger.github.io/aorsf/reference/aorsf-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"aorsf: Accelerated Oblique Random Survival Forests — aorsf-package","text":"Maintainer: Byron Jaeger bjaeger@wakehealth.edu (ORCID) contributors: Nicholas Pajewski [contributor] Sawyer Welden swelden@wakehealth.edu [contributor] Christopher Jackson chris.jackson@mrc-bsu.cam.ac.uk [reviewer] Marvin Wright [reviewer] Lukas Burk [reviewer]","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/as.data.table.orsf_summary_uni.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce to data.table — as.data.table.orsf_summary_uni","title":"Coerce to data.table — as.data.table.orsf_summary_uni","text":"Convert 'orsf_summary' object data.table object.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/as.data.table.orsf_summary_uni.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce to data.table — as.data.table.orsf_summary_uni","text":"","code":"# S3 method for orsf_summary_uni as.data.table(x, ...)"},{"path":"https://bcjaeger.github.io/aorsf/reference/as.data.table.orsf_summary_uni.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce to data.table — as.data.table.orsf_summary_uni","text":"x object class 'orsf_summary_uni' ... used","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/as.data.table.orsf_summary_uni.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce to data.table — as.data.table.orsf_summary_uni","text":"data.table","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/as.data.table.orsf_summary_uni.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce to data.table — as.data.table.orsf_summary_uni","text":"","code":"library(data.table) object <- orsf(pbc_orsf, Surv(time, status) ~ . - id) smry <- orsf_summarize_uni(object, n_variables = 3) as.data.table(smry) #> variable importance value mean medn lwr upr #> 1: bili 0.11307855 0.80 0.2371490 0.1202006 0.05116040 0.3821702 #> 2: bili 0.11307855 1.4 0.2651162 0.1487302 0.06791377 0.4193537 #> 3: bili 0.11307855 3.5 0.3720891 0.2833817 0.15276030 0.5680952 #> 4: copper 0.05610062 43 0.2651930 0.1403332 0.05421519 0.4502802 #> 5: copper 0.05610062 74 0.2926748 0.1647021 0.07409095 0.5005956 #> 6: copper 0.05610062 129 0.3539127 0.2412332 0.12181162 0.5944691 #> 7: sex 0.03438589 m 0.3541657 0.2294593 0.11517133 0.5905949 #> 8: sex 0.03438589 f 0.3049946 0.1560052 0.05105105 0.5552089 #> pred_horizon level #> 1: 1788 <NA> #> 2: 1788 <NA> #> 3: 1788 <NA> #> 4: 1788 <NA> #> 5: 1788 <NA> #> 6: 1788 <NA> #> 7: 1788 m #> 8: 1788 f"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":null,"dir":"Reference","previous_headings":"","what":"Oblique Random Survival Forest (ORSF) — orsf","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"Fit oblique random survival forest","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"","code":"orsf( data, formula, control = orsf_control_fast(), weights = NULL, n_tree = 500, n_split = 5, n_retry = 3, n_thread = 1, mtry = NULL, sample_with_replacement = TRUE, sample_fraction = 0.632, leaf_min_events = 1, leaf_min_obs = 5, split_rule = \"logrank\", split_min_events = 5, split_min_obs = 10, split_min_stat = switch(split_rule, logrank = 3.841459, cstat = 0.5), oobag_pred_type = \"surv\", oobag_pred_horizon = NULL, oobag_eval_every = n_tree, oobag_fun = NULL, importance = \"anova\", group_factors = TRUE, tree_seeds = NULL, attach_data = TRUE, no_fit = FALSE, na_action = \"fail\", verbose_progress = FALSE, ... ) orsf_train(object)"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"data data.frame, tibble, data.table contains relevant variables. formula (formula) response left hand side include time variable, followed status variable, may written inside call Surv (see examples). terms right names predictor variables. control (orsf_control) object returned one orsf_control functions: orsf_control_fast (default) uses single iteration Newton Raphson scoring identify linear combination predictors. orsf_control_cph uses Newton Raphson scoring convergence criteria met. orsf_control_net uses glmnet identify linear combinations predictors, similar Jaeger (2019). orsf_control_custom allows user apply function create linear combinations predictors. weights (numeric vector) Optional. given, input length equal nrow(data). Values weights treated like replication weights, .e., value 2 thing 2 observations data, containing copy corresponding person's data. Use weights cautiously, orsf count number observations events prior growing node tree, higher values weights lead deeper trees. n_tree (integer) number trees grow. Default n_tree = 500. n_split (integer) number cut-points assessed splitting node decision trees. Default n_split = 5. n_retry (integer) node can split, current linear combination inputs unable provide valid split, orsf try new linear combination based different set randomly selected predictors, n_retry times. Default n_retry = 3. Set n_retry = 0 prevent retries. n_thread (integer) number threads use growing trees, computing predictions, computing importance. Default one thread. use maximum number threads system provides concurrent execution, set n_thread = 0. mtry (integer) Number predictors randomly included candidates splitting node. default smallest integer greater square root number total predictors, .e., mtry = ceiling(sqrt(number predictors)) sample_with_replacement (logical) TRUE (default), observations sampled replacement -bag sample created decision tree. FALSE, observations sampled without replacement tree -bag sample containing sample_fraction% original sample. sample_fraction (double) proportion observations trees' -bag sample contain, relative number rows data. used sample_with_replacement FALSE. Default value 0.632. leaf_min_events (integer) minimum number events leaf node. Default leaf_min_events = 1 leaf_min_obs (integer) minimum number observations leaf node. Default leaf_min_obs = 5. split_rule (character) assess quality potential splitting rule node. Valid options 'logrank' : log-rank test statistic. 'cstat' : Harrell's concordance statistic. split_min_events (integer) minimum number events required node consider splitting . Default split_min_events = 5 split_min_obs (integer) minimum number observations required node consider splitting . Default split_min_obs = 10. split_min_stat (double) minimum test statistic required split node. Default 3.841459 split_rule = 'logrank' 0.50 split_rule = 'cstat'. splits found statistic exceeding split_min_stat, given node either becomes leaf retry occurs (n_retry retries). oobag_pred_type (character) type --bag predictions compute fitting ensemble. Valid options 'none' : compute --bag predictions 'risk' : probability event occurring oobag_pred_horizon. 'surv' : 1 - risk. 'chf' : cumulative hazard function oobag_pred_horizon. 'mort' : mortality, .e., number events expected observations training data identical given observation. oobag_pred_horizon (numeric) numeric value indicating time used --bag predictions. Default median observed times, .e., oobag_pred_horizon = median(time). oobag_eval_every (integer) --bag performance ensemble checked every oobag_eval_every trees. , oobag_eval_every = 10, --bag performance checked growing 10th tree, 20th tree, . Default oobag_eval_every = n_tree. oobag_fun (function) used evaluating --bag prediction accuracy every oobag_eval_every trees. oobag_fun = NULL (default), Harrell's C-statistic (1982) used evaluate accuracy. use oobag_fun note following: oobag_fun two inputs: y_mat s_vec y_mat two column matrix first column named 'time', second named 'status' s_vec numeric vector containing predicted survival probabilities. oobag_fun return numeric output length 1 details, see --bag vignette. importance (character) Indicate method variable importance: 'none': variable importance computed. 'anova': compute analysis variance (ANOVA) importance 'negate': compute negation importance 'permute': compute permutation importance details methods, see orsf_vi. group_factors (logical) relevant variable importance estimated. TRUE, importance factor variables reported overall aggregating importance individual levels factor. FALSE, importance individual factor levels returned. tree_seeds (integer vector) Optional. specified, random seeds set using values tree_seeds[] growing tree . Two forests grown number trees seeds exact --bag samples, making --bag error estimates forests comparable. NULL (default), seeds set training process. attach_data (logical) TRUE, copy training data attached output. helpful plan using functions like orsf_pd_oob orsf_summarize_uni interpret forest using training data. Default TRUE. no_fit (logical) TRUE, model fitting steps defined saved, training initiated. object returned can directly submitted orsf_train() long attach_data TRUE. na_action (character) happen data contains missing values (.e., NA values). Valid options : 'fail' : error thrown data contains NA values 'omit' : rows data incomplete data dropped 'impute_meanmode' : missing values continuous categorical variables data imputed using mean mode, respectively. Note option selected attach_data TRUE, data attached output imputed version data. verbose_progress (logical) TRUE, progress messages printed console. FALSE (default), nothing printed. ... arguments passed methods (currently used). object untrained 'aorsf' object, created setting no_fit = TRUE orsf().","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"accelerated oblique RSF object (aorsf)","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"function based similar ORSF function obliqueRSF R package. primary difference function runs much faster. speed increase attributable better management memory (.e., unnecessary copies inputs) using Newton Raphson scoring algorithm identify linear combinations inputs rather performing penalized regression using routines glmnet.modified Newton Raphson scoring algorithm function applies adaptation C++ routine developed Terry M. Therneau fits Cox proportional hazards models (see survival::coxph() specifically survival::coxph.fit()).","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"details-on-inputs","dir":"Reference","previous_headings":"","what":"Details on inputs","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"formula: response formula can survival object returned Surv function, can also just time status variables. .e., Surv(time, status) ~ . works just like time + status ~ . . symbol right hand side short-hand using variables data (omitting left hand side formula) predictors. order variables left hand side matters. .e., writing status + time ~ . make orsf assume status variable actually time variable. response variable can survival object stored data. example, y ~ . valid formula data$y inherits Surv class. Although can fit oblique random survival forest 1 predictor variable, formula least 2 predictors. reason recommendation linear combination predictors trivial one predictor. mtry: mtry parameter may temporarily reduced ensure least 2 events per predictor variable. occurs using orsf_control_cph coefficients Newton Raphson scoring algorithm may become unstable number covariates greater equal number events. reduction occur using orsf_control_net. oobag_fun: oobag_fun specified, used compute negation importance permutation importance, role ANOVA importance. n_thread: R function must called C++ (.e., user-supplied function compute --bag error identify linear combinations variables), n_thread automatically set 1 attempting run R functions multiple threads cause R session crash.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"what-is-an-oblique-decision-tree-","dir":"Reference","previous_headings":"","what":"What is an oblique decision tree?","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"Decision trees developed splitting set training data two new subsets, goal similarity within new subsets . splitting process repeated resulting subsets data stopping criterion met. new subsets data formed based single predictor, decision tree said axis-based splits data appear perpendicular axis predictor. linear combinations variables used instead single variable, tree oblique splits data neither parallel right angle axis Figure : Decision trees classification axis-based splitting (left) oblique splitting (right). Cases orange squares; controls purple circles. trees partition predictor space defined variables X1 X2, oblique splits better job separating two classes.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"what-is-a-random-forest-","dir":"Reference","previous_headings":"","what":"What is a random forest?","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"Random forests collections de-correlated decision trees. Predictions tree aggregated make ensemble prediction forest. details, see Breiman el, 2001.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"training-out-of-bag-error-and-testing","dir":"Reference","previous_headings":"","what":"Training, out-of-bag error, and testing","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"random forests, tree grown bootstrapped version training set. bootstrap samples selected replacement, bootstrapped training set contains two-thirds instances original training set. '--bag' data instances bootstrapped training set. tree random forest can make predictions --bag data, --bag predictions can aggregated make ensemble --bag prediction. Since --bag data used grow tree, accuracy ensemble --bag predictions approximate generalization error random forest. Generalization error refers error random forest's predictions applied predict outcomes data used train , .e., testing data.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"missing-data","dir":"Reference","previous_headings":"","what":"Missing data","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"Data passed aorsf functions allowed missing values. user impute missing values using R package purpose, recipes mlr3pipelines.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"First load relevant packages entry-point aorsf standard call orsf(): printing fit provides quick descriptive summaries:","code":"set.seed(329730) suppressPackageStartupMessages({ library(aorsf) library(survival) library(tidymodels) library(tidyverse) library(randomForestSRC) library(ranger) library(riskRegression) library(obliqueRSF) }) fit <- orsf(pbc_orsf, Surv(time, status) ~ . - id) fit ## ---------- Oblique random survival forest ## ## Linear combinations: Accelerated ## N observations: 276 ## N events: 111 ## N trees: 500 ## N predictors total: 17 ## N predictors per node: 5 ## Average leaves per tree: 25 ## Min observations in leaf: 5 ## Min events in leaf: 1 ## OOB stat value: 0.84 ## OOB stat type: Harrell's C-statistic ## Variable importance: anova ## ## -----------------------------------------"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"model-control","dir":"Reference","previous_headings":"","what":"Model control","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"examples make use orsf_control_ functions build compare models based --bag predictions. also standardize --bag samples using input argument tree_seeds","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"accelerated-linear-combinations","dir":"Reference","previous_headings":"","what":"Accelerated linear combinations","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"accelerated ORSF ensemble default nice balance computational speed prediction accuracy. runs single iteration Newton Raphson scoring Cox partial likelihood function find linear combinations predictors.","code":"fit_accel <- orsf(pbc_orsf, control = orsf_control_fast(), formula = Surv(time, status) ~ . - id, tree_seeds = 1:500)"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"linear-combinations-with-cox-regression","dir":"Reference","previous_headings":"","what":"Linear combinations with Cox regression","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"orsf_control_cph runs Cox regression non-terminal node survival tree, using regression coefficients create linear combinations predictors:","code":"fit_cph <- orsf(pbc_orsf, control = orsf_control_cph(), formula = Surv(time, status) ~ . - id, tree_seeds = 1:500)"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"linear-combinations-with-penalized-cox-regression","dir":"Reference","previous_headings":"","what":"Linear combinations with penalized cox regression","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"orsf_control_net runs penalized Cox regression non-terminal node survival tree, using regression coefficients create linear combinations predictors. can really helpful want feature selection within node, lot slower options.","code":"fit_net <- orsf(pbc_orsf, # select 3 predictors out of 5 to be used in # each linear combination of predictors. control = orsf_control_net(df_target = 3), formula = Surv(time, status) ~ . - id, tree_seeds = 1:500)"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"linear-combinations-with-your-own-function","dir":"Reference","previous_headings":"","what":"Linear combinations with your own function","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"Let’s make two customized functions identify linear combinations predictors. first uses random coefficients second derives coefficients principal component analysis. third uses orsf() inside orsf() (aka reinforcement learning trees RLTs). can plug functions orsf_control_custom(), pass result orsf(): fit seems work best example? Let’s find evaluating --bag survival predictions. AUC values, highest lowest: indices prediction accuracy: inspection, glmnet approach highest discrimination index prediction accuracy. random coefficients don’t well, aren’t bad.","code":"f_rando <- function(x_node, y_node, w_node){ matrix(runif(ncol(x_node)), ncol=1) } f_pca <- function(x_node, y_node, w_node) { # estimate two principal components. pca <- stats::prcomp(x_node, rank. = 2) # use the second principal component to split the node pca$rotation[, 1L, drop = FALSE] } # some special care is taken to prevent your R session from crashing. # Specifically, random coefficients are used when n_obs <= 10 # or n_events <= 5. f_aorsf <- function(x_node, y_node, w_node){ colnames(y_node) <- c('time', 'status') colnames(x_node) <- paste(\"x\", seq(ncol(x_node)), sep = '') data <- as.data.frame(cbind(y_node, x_node)) if(nrow(data) <= 10 || sum(y_node[,'status']) <= 5) return(matrix(runif(ncol(x_node)), ncol = 1)) fit <- orsf(data, time + status ~ ., weights = as.numeric(w_node), n_tree = 25, importance = 'anova') out <- orsf_vi(fit)[colnames(x_node)] matrix(out, ncol = 1) } fit_rando <- orsf(pbc_orsf, Surv(time, status) ~ . - id, control = orsf_control_custom(beta_fun = f_rando), tree_seeds = 1:500) fit_pca <- orsf(pbc_orsf, Surv(time, status) ~ . - id, control = orsf_control_custom(beta_fun = f_pca), tree_seeds = 1:500) fit_rlt <- orsf(pbc_orsf, time + status ~ . - id, control = orsf_control_custom(beta_fun = f_aorsf), tree_seeds = 1:500) risk_preds <- list( accel = 1 - fit_accel$pred_oobag, cph = 1 - fit_cph$pred_oobag, net = 1 - fit_net$pred_oobag, rando = 1 - fit_rando$pred_oobag, pca = 1 - fit_pca$pred_oobag, rlt = 1 - fit_rlt$pred_oobag ) sc <- Score(object = risk_preds, formula = Surv(time, status) ~ 1, data = pbc_orsf, summary = 'IPA', times = fit_accel$pred_horizon) sc$AUC$score[order(-AUC)] ## model times AUC se lower upper ## 1: net 1788 0.9179396 0.02012887 0.8784877 0.9573915 ## 2: accel 1788 0.9106396 0.02076004 0.8699507 0.9513286 ## 3: cph 1788 0.9061167 0.02277540 0.8614777 0.9507556 ## 4: rlt 1788 0.9012605 0.02178982 0.8585533 0.9439678 ## 5: rando 1788 0.8997729 0.02201363 0.8566270 0.9429188 ## 6: pca 1788 0.8996927 0.02245483 0.8556821 0.9437034 sc$Brier$score[order(-IPA), .(model, times, IPA)] ## model times IPA ## 1: net 1788 0.5020652 ## 2: cph 1788 0.4759061 ## 3: accel 1788 0.4743392 ## 4: pca 1788 0.4398468 ## 5: rlt 1788 0.4373910 ## 6: rando 1788 0.4219209 ## 7: Null model 1788 0.0000000"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"tidymodels","dir":"Reference","previous_headings":"","what":"tidymodels","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"example uses tidymodels functions stops short using official tidymodels workflow. working getting aorsf pulled censored package update real workflows happens!","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"comparing-orsf-with-other-learners","dir":"Reference","previous_headings":"","what":"Comparing ORSF with other learners","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"Start recipe pre-process data Next create 10-fold cross validation object pre-process data: Define functions ‘workflow’ randomForestSRC, ranger, aorsf. Run ‘workflows’ fold: Next unnest column get back tibble testing data predictions. finish aggregating predictions computing performance testing data. Note computing one statistic predictions instead computing one statistic fold. approach fine smaller testing sets /small event counts. inspection, aorsf obtained slightly higher discrimination (AUC) aorsf obtained higher index prediction accuracy (IPA)","code":"imputer <- recipe(pbc_orsf, formula = time + status ~ .) %>% step_impute_mean(all_numeric_predictors()) %>% step_impute_mode(all_nominal_predictors()) # 10-fold cross validation; make a container for the pre-processed data analyses <- vfold_cv(data = pbc_orsf, v = 10) %>% mutate(recipe = map(splits, ~prep(imputer, training = training(.x))), train = map(recipe, juice), test = map2(splits, recipe, ~bake(.y, new_data = testing(.x)))) analyses ## # 10-fold cross-validation ## # A tibble: 10 x 5 ## splits id recipe train test ## <list> <chr> <list> <list> <list> ## 1 <split [248/28]> Fold01 <recipe> <tibble [248 x 20]> <tibble [28 x 20]> ## 2 <split [248/28]> Fold02 <recipe> <tibble [248 x 20]> <tibble [28 x 20]> ## 3 <split [248/28]> Fold03 <recipe> <tibble [248 x 20]> <tibble [28 x 20]> ## 4 <split [248/28]> Fold04 <recipe> <tibble [248 x 20]> <tibble [28 x 20]> ## 5 <split [248/28]> Fold05 <recipe> <tibble [248 x 20]> <tibble [28 x 20]> ## 6 <split [248/28]> Fold06 <recipe> <tibble [248 x 20]> <tibble [28 x 20]> ## 7 <split [249/27]> Fold07 <recipe> <tibble [249 x 20]> <tibble [27 x 20]> ## 8 <split [249/27]> Fold08 <recipe> <tibble [249 x 20]> <tibble [27 x 20]> ## 9 <split [249/27]> Fold09 <recipe> <tibble [249 x 20]> <tibble [27 x 20]> ## 10 <split [249/27]> Fold10 <recipe> <tibble [249 x 20]> <tibble [27 x 20]> rfsrc_wf <- function(train, test, pred_horizon){ # rfsrc does not like tibbles, so cast input data into data.frames train <- as.data.frame(train) test <- as.data.frame(test) rfsrc(formula = Surv(time, status) ~ ., data = train) %>% predictRisk(newdata = test, times = pred_horizon) %>% as.numeric() } ranger_wf <- function(train, test, pred_horizon){ ranger(Surv(time, status) ~ ., data = train) %>% predictRisk(newdata = test, times = pred_horizon) %>% as.numeric() } aorsf_wf <- function(train, test, pred_horizon){ train %>% orsf(Surv(time, status) ~ .,) %>% predict(new_data = test, pred_horizon = pred_horizon) %>% as.numeric() } # 5 year risk prediction ph <- 365.25 * 5 results <- analyses %>% transmute(test, pred_aorsf = map2(train, test, aorsf_wf, pred_horizon = ph), pred_rfsrc = map2(train, test, rfsrc_wf, pred_horizon = ph), pred_ranger = map2(train, test, ranger_wf, pred_horizon = ph)) results <- results %>% unnest(everything()) glimpse(results) ## Rows: 276 ## Columns: 23 ## $ id <int> 16, 29, 43, 62, 79, 82, 103, 105, 111, 114, 115, 139, 141,~ ## $ trt <fct> placebo, placebo, d_penicill_main, placebo, d_penicill_mai~ ## $ age <dbl> 40.44353, 63.87680, 48.87064, 60.70637, 46.51608, 67.31006~ ## $ sex <fct> f, f, f, f, f, f, f, f, f, m, f, f, f, f, f, f, f, f, f, f~ ## $ ascites <fct> 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0~ ## $ hepato <fct> 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1~ ## $ spiders <fct> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1~ ## $ edema <fct> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~ ## $ bili <dbl> 0.7, 0.7, 1.1, 1.3, 0.8, 4.5, 2.5, 1.1, 5.5, 3.2, 0.7, 1.1~ ## $ chol <int> 204, 370, 361, 302, 315, 472, 188, 464, 528, 259, 303, 328~ ## $ albumin <dbl> 3.66, 3.78, 3.64, 2.75, 4.24, 4.09, 3.67, 4.20, 4.18, 4.30~ ## $ copper <int> 28, 24, 36, 58, 13, 154, 57, 38, 77, 208, 81, 159, 59, 76,~ ## $ alk.phos <dbl> 685.0, 5833.0, 5430.2, 1523.0, 1637.0, 1580.0, 1273.0, 164~ ## $ ast <dbl> 72.85, 73.53, 67.08, 43.40, 170.50, 117.80, 119.35, 151.90~ ## $ trig <int> 58, 86, 89, 112, 70, 272, 102, 102, 78, 78, 156, 134, 56, ~ ## $ platelet <int> 198, 390, 203, 329, 426, 412, 110, 348, 467, 268, 307, 142~ ## $ protime <dbl> 10.8, 10.6, 10.6, 13.2, 10.9, 11.1, 11.1, 10.3, 10.7, 11.7~ ## $ stage <ord> 3, 2, 2, 4, 3, 3, 4, 3, 3, 3, 3, 4, 2, 2, 3, 4, 2, 3, 4, 4~ ## $ time <int> 3672, 4509, 4556, 3090, 3707, 3574, 110, 3092, 2350, 3395,~ ## $ status <dbl> 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0~ ## $ pred_aorsf <dbl> 0.02210163, 0.12510110, 0.07571520, 0.59580668, 0.12839078~ ## $ pred_rfsrc <dbl> 0.01861595, 0.15632904, 0.07635485, 0.62281617, 0.19145913~ ## $ pred_ranger <dbl> 0.02143363, 0.13367920, 0.05892584, 0.54481330, 0.21380654~ Score( object = list(aorsf = results$pred_aorsf, rfsrc = results$pred_rfsrc, ranger = results$pred_ranger), formula = Surv(time, status) ~ 1, data = results, summary = 'IPA', times = ph ) ## ## Metric AUC: ## ## Results by model: ## ## model times AUC lower upper ## 1: aorsf 1826 91.0 86.8 95.2 ## 2: rfsrc 1826 89.2 84.8 93.7 ## 3: ranger 1826 89.6 85.3 94.0 ## ## Results of model comparisons: ## ## times model reference delta.AUC lower upper p ## 1: 1826 rfsrc aorsf -1.7 -3.4 -0.1 0.04 ## 2: 1826 ranger aorsf -1.3 -2.9 0.2 0.08 ## 3: 1826 ranger rfsrc 0.4 -0.8 1.6 0.52 ## ## NOTE: Values are multiplied by 100 and given in %. ## NOTE: The higher AUC the better. ## ## Metric Brier: ## ## Results by model: ## ## model times Brier lower upper IPA ## 1: Null model 1826.25 20.5 18.1 22.9 0.0 ## 2: aorsf 1826.25 10.9 8.7 13.1 46.9 ## 3: rfsrc 1826.25 12.0 9.9 14.2 41.3 ## 4: ranger 1826.25 12.0 9.9 14.1 41.5 ## ## Results of model comparisons: ## ## times model reference delta.Brier lower upper p ## 1: 1826.25 aorsf Null model -9.6 -12.2 -7.0 9.364941e-13 ## 2: 1826.25 rfsrc Null model -8.5 -10.7 -6.2 2.074175e-13 ## 3: 1826.25 ranger Null model -8.5 -10.8 -6.2 3.712823e-13 ## 4: 1826.25 rfsrc aorsf 1.1 0.3 2.0 1.075856e-02 ## 5: 1826.25 ranger aorsf 1.1 0.3 1.9 4.825778e-03 ## 6: 1826.25 ranger rfsrc -0.1 -0.6 0.5 8.429772e-01 ## ## NOTE: Values are multiplied by 100 and given in %. ## NOTE: The lower Brier the better, the higher IPA the better."},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"mlr-pipelines","dir":"Reference","previous_headings":"","what":"mlr3 pipelines","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"Warning: code may may run depending current version mlr3proba. First load additional mlr3 libraries. Next ’ll define tasks learners engage . Now can make benchmark designed compare three favorite learners: Let’s look overall results: inspection, aorsf higher expected value ‘surv.cindex’ (higher better) aorsf lower expected value ‘surv.graf’ (lower better)","code":"suppressPackageStartupMessages({ library(mlr3verse) library(mlr3proba) library(mlr3extralearners) library(mlr3viz) library(mlr3benchmark) }) # Mayo Clinic Primary Biliary Cholangitis Data task_pbc <- TaskSurv$new( id = 'pbc', backend = select(pbc_orsf, -id) %>% mutate(stage = as.numeric(stage)), time = \"time\", event = \"status\" ) # Veteran's Administration Lung Cancer Trial data(veteran, package = \"randomForestSRC\") task_veteran <- TaskSurv$new( id = 'veteran', backend = veteran, time = \"time\", event = \"status\" ) # NKI 70 gene signature data_nki <- OpenML::getOMLDataSet(data.id = 1228) task_nki <- TaskSurv$new( id = 'nki', backend = data_nki$data, time = \"time\", event = \"event\" ) # Gene Expression-Based Survival Prediction in Lung Adenocarcinoma data_lung <- OpenML::getOMLDataSet(data.id = 1245) task_lung <- TaskSurv$new( id = 'nki', backend = data_lung$data %>% mutate(OS_event = as.numeric(OS_event) -1), time = \"OS_years\", event = \"OS_event\" ) # Chemotherapy for Stage B/C colon cancer # (there are two rows per person, one for death # and the other for recurrence, hence the two tasks) task_colon_death <- TaskSurv$new( id = 'colon_death', backend = survival::colon %>% filter(etype == 2) %>% drop_na() %>% # drop id, redundant variables select(-id, -study, -node4, -etype), mutate(OS_event = as.numeric(OS_event) -1), time = \"time\", event = \"status\" ) task_colon_recur <- TaskSurv$new( id = 'colon_death', backend = survival::colon %>% filter(etype == 1) %>% drop_na() %>% # drop id, redundant variables select(-id, -study, -node4, -etype), mutate(OS_event = as.numeric(OS_event) -1), time = \"time\", event = \"status\" ) # putting them all together tasks <- list(task_pbc, task_veteran, task_nki, task_lung, task_colon_death, task_colon_recur, # add a few more pre-made ones tsk(\"actg\"), tsk('gbcs'), tsk('grace'), tsk(\"unemployment\"), tsk(\"whas\")) # Learners with default parameters learners <- lrns(c(\"surv.ranger\", \"surv.rfsrc\", \"surv.aorsf\")) # Brier (Graf) score, c-index and training time as measures measures <- msrs(c(\"surv.graf\", \"surv.cindex\", \"time_train\")) # Benchmark with 5-fold CV design <- benchmark_grid( tasks = tasks, learners = learners, resamplings = rsmps(\"cv\", folds = 5) ) benchmark_result <- benchmark(design) bm_scores <- benchmark_result$score(measures, predict_sets = \"test\") bm_scores %>% select(task_id, learner_id, surv.graf, surv.cindex, time_train) %>% group_by(learner_id) %>% filter(!is.infinite(surv.graf)) %>% summarize( across( .cols = c(surv.graf, surv.cindex, time_train), .fns = mean, na.rm = TRUE ) ) ## # A tibble: 3 x 4 ## learner_id surv.graf surv.cindex time_train ## <chr> <dbl> <dbl> <dbl> ## 1 surv.aorsf 0.152 0.733 1.41 ## 2 surv.ranger 0.166 0.712 1.95 ## 3 surv.rfsrc 0.155 0.723 0.745"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Oblique Random Survival Forest (ORSF) — orsf","text":"Harrell FE, Califf RM, Pryor DB, Lee KL, Rosati RA. Evaluating Yield Medical Tests. JAMA 1982; 247(18):2543-2546. DOI: 10.1001/jama.1982.03320430047030 Breiman L. Random forests. Machine learning 2001 Oct; 45(1):5-32. DOI: 10.1023/:1010933404324 Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS. Random survival forests. Annals applied statistics 2008 Sep; 2(3):841-60. DOI: 10.1214/08-AOAS169 Jaeger BC, Long DL, Long DM, Sims M, Szychowski JM, Min YI, Mcclure LA, Howard G, Simon N. Oblique random survival forests. Annals applied statistics 2019 Sep; 13(3):1847-83. DOI: 10.1214/19-AOAS1261 Jaeger BC, Welden S, Lenoir K, Speiser JL, Segar MW, Pandey , Pajewski NM. Accelerated interpretable oblique random survival forests. arXiv e-prints 2022 Aug; arXiv-2208. URL: https://arxiv.org/abs/2208.01129","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_cph.html","id":null,"dir":"Reference","previous_headings":"","what":"Cox regression ORSF control — orsf_control_cph","title":"Cox regression ORSF control — orsf_control_cph","text":"Use coefficients proportional hazards model create linear combinations predictor variables fitting orsf model.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_cph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cox regression ORSF control — orsf_control_cph","text":"","code":"orsf_control_cph(method = \"efron\", eps = 1e-09, iter_max = 20, ...)"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_cph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cox regression ORSF control — orsf_control_cph","text":"method (character) character string specifying method tie handling. ties, methods equivalent. Valid options 'breslow' 'efron'. Efron approximation default accurate dealing tied event times similar computational efficiency compared Breslow method. eps (double) using Newton Raphson scoring identify linear combinations inputs, iteration continues algorithm relative change log partial likelihood less eps, absolute change less sqrt(eps). Must positive. default value 1e-09 used consistency survival::coxph.control. iter_max (integer) iteration continues convergence (see eps ) number attempted iterations equal iter_max. ... arguments passed methods (currently used).","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_cph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cox regression ORSF control — orsf_control_cph","text":"object class 'orsf_control', used input control argument orsf.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_cph.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cox regression ORSF control — orsf_control_cph","text":"code survival package modified make routine. details Cox proportional hazards model, see coxph /Therneau Grambsch (2000).","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_cph.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Cox regression ORSF control — orsf_control_cph","text":"Therneau T.M., Grambsch P.M. (2000) Cox Model. : Modeling Survival Data: Extending Cox Model. Statistics Biology Health. Springer, New York, NY. DOI: 10.1007/978-1-4757-3294-8_3","code":""},{"path":[]},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_cph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cox regression ORSF control — orsf_control_cph","text":"","code":"orsf(data = pbc_orsf, formula = Surv(time, status) ~ . - id, control = orsf_control_cph()) #> ---------- Oblique random survival forest #> #> Linear combinations: Cox regression #> N observations: 276 #> N events: 111 #> N trees: 500 #> N predictors total: 17 #> N predictors per node: 5 #> Average leaves per tree: 25 #> Min observations in leaf: 5 #> Min events in leaf: 1 #> OOB stat value: 0.84 #> OOB stat type: Harrell's C-statistic #> Variable importance: anova #> #> -----------------------------------------"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_custom.html","id":null,"dir":"Reference","previous_headings":"","what":"Custom ORSF control — orsf_control_custom","title":"Custom ORSF control — orsf_control_custom","text":"Custom ORSF control","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_custom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Custom ORSF control — orsf_control_custom","text":"","code":"orsf_control_custom(beta_fun, ...)"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_custom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Custom ORSF control — orsf_control_custom","text":"beta_fun (function) function define coefficients used linear combinations predictor variables. beta_fun must accept three inputs named x_node, y_node w_node, expect following types dimensions: x_node (matrix; n rows, p columns) y_node (matrix; n rows, 2 columns) w_node (matrix; n rows, 1 column) addition, beta_fun must return matrix p rows 1 column. conditions met, orsf_control_custom() let know. ... arguments passed methods (currently used).","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_custom.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Custom ORSF control — orsf_control_custom","text":"object class 'orsf_control', used input control argument orsf.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_custom.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Custom ORSF control — orsf_control_custom","text":"Two customized functions identify linear combinations predictors shown . first uses random coefficients second derives coefficients principal component analysis.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_custom.html","id":"random-coefficients","dir":"Reference","previous_headings":"","what":"Random coefficients","title":"Custom ORSF control — orsf_control_custom","text":"f_rando() function get random coefficients: can plug f_rando orsf_control_custom(), pass result orsf():","code":"f_rando <- function(x_node, y_node, w_node){ matrix(runif(ncol(x_node)), ncol=1) } library(aorsf) fit_rando <- orsf(pbc_orsf, Surv(time, status) ~ . - id, control = orsf_control_custom(beta_fun = f_rando), n_tree = 500) fit_rando ## ---------- Oblique random survival forest ## ## Linear combinations: Custom user function ## N observations: 276 ## N events: 111 ## N trees: 500 ## N predictors total: 17 ## N predictors per node: 5 ## Average leaves per tree: 20 ## Min observations in leaf: 5 ## Min events in leaf: 1 ## OOB stat value: 0.84 ## OOB stat type: Harrell's C-statistic ## Variable importance: anova ## ## -----------------------------------------"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_custom.html","id":"principal-components","dir":"Reference","previous_headings":"","what":"Principal components","title":"Custom ORSF control — orsf_control_custom","text":"Follow steps , starting custom function: plug function orsf_control_custom() pass result orsf():","code":"f_pca <- function(x_node, y_node, w_node) { # estimate two principal components. pca <- stats::prcomp(x_node, rank. = 2) # use the second principal component to split the node pca$rotation[, 2L, drop = FALSE] } fit_pca <- orsf(pbc_orsf, Surv(time, status) ~ . - id, control = orsf_control_custom(beta_fun = f_pca), n_tree = 500)"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_custom.html","id":"evaluate","dir":"Reference","previous_headings":"","what":"Evaluate","title":"Custom ORSF control — orsf_control_custom","text":"well two customized ORSFs ? Let’s compute indices prediction accuracy based --bag predictions: PCA ORSF quite well! (higher IPA better)","code":"library(riskRegression) library(survival) risk_preds <- list(rando = 1 - fit_rando$pred_oobag, pca = 1 - fit_pca$pred_oobag) sc <- Score(object = risk_preds, formula = Surv(time, status) ~ 1, data = pbc_orsf, summary = 'IPA', times = fit_pca$pred_horizon) sc$Brier ## ## Results by model: ## ## model times Brier lower upper IPA ## 1: Null model 1788 20.479 18.090 22.868 0.000 ## 2: rando 1788 11.554 9.476 13.631 43.584 ## 3: pca 1788 12.673 10.692 14.654 38.118 ## ## Results of model comparisons: ## ## times model reference delta.Brier lower upper p ## 1: 1788 rando Null model -8.926 -11.071 -6.780 3.491749e-16 ## 2: 1788 pca Null model -7.806 -9.534 -6.079 8.192570e-19 ## 3: 1788 pca rando 1.119 0.350 1.889 4.354090e-03 ## ## NOTE: Values are multiplied by 100 and given in %. ## NOTE: The lower Brier the better, the higher IPA the better."},{"path":[]},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_fast.html","id":null,"dir":"Reference","previous_headings":"","what":"Accelerated ORSF control — orsf_control_fast","title":"Accelerated ORSF control — orsf_control_fast","text":"Accelerated ORSF control","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_fast.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Accelerated ORSF control — orsf_control_fast","text":"","code":"orsf_control_fast(method = \"efron\", do_scale = TRUE, ...)"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_fast.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Accelerated ORSF control — orsf_control_fast","text":"method (character) character string specifying method tie handling. ties, methods equivalent. Valid options 'breslow' 'efron'. Efron approximation default accurate dealing tied event times similar computational efficiency compared Breslow method. do_scale (logical) TRUE, values predictors scaled prior instance Newton Raphson scoring, using summary values data current node decision tree. ... arguments passed methods (currently used).","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_fast.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Accelerated ORSF control — orsf_control_fast","text":"object class 'orsf_control', used input control argument orsf.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_fast.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Accelerated ORSF control — orsf_control_fast","text":"code survival package modified make routine. Adjust do_scale risk. Setting do_scale = FALSE reduce computation time also make orsf model dependent scale data, default value TRUE. good idea center scale predictors prior running orsf() plan setting do_scale = FALSE.","code":""},{"path":[]},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_fast.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Accelerated ORSF control — orsf_control_fast","text":"","code":"orsf(data = pbc_orsf, formula = Surv(time, status) ~ . - id, control = orsf_control_fast()) #> ---------- Oblique random survival forest #> #> Linear combinations: Accelerated #> N observations: 276 #> N events: 111 #> N trees: 500 #> N predictors total: 17 #> N predictors per node: 5 #> Average leaves per tree: 25 #> Min observations in leaf: 5 #> Min events in leaf: 1 #> OOB stat value: 0.84 #> OOB stat type: Harrell's C-statistic #> Variable importance: anova #> #> -----------------------------------------"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_net.html","id":null,"dir":"Reference","previous_headings":"","what":"Penalized Cox regression ORSF control — orsf_control_net","title":"Penalized Cox regression ORSF control — orsf_control_net","text":"Penalized Cox regression ORSF control","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_net.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Penalized Cox regression ORSF control — orsf_control_net","text":"","code":"orsf_control_net(alpha = 1/2, df_target = NULL, ...)"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_net.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Penalized Cox regression ORSF control — orsf_control_net","text":"alpha (double) elastic net mixing parameter. value 1 gives lasso penalty, value 0 gives ridge penalty. multiple values alpha given, penalized model fit using alpha value prior splitting node. df_target (integer) Preferred number variables used linear combination. ... arguments passed methods (currently used).","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_net.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Penalized Cox regression ORSF control — orsf_control_net","text":"object class 'orsf_control', used input control argument orsf.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_net.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Penalized Cox regression ORSF control — orsf_control_net","text":"df_target less mtry, separate argument orsf indicates number variables chosen random prior finding linear combination variables.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_net.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Penalized Cox regression ORSF control — orsf_control_net","text":"Simon N, Friedman J, Hastie T, Tibshirani R. Regularization paths Cox's proportional hazards model via coordinate descent. Journal statistical software 2011 Mar; 39(5):1. DOI: 10.18637/jss.v039.i05","code":""},{"path":[]},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_control_net.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Penalized Cox regression ORSF control — orsf_control_net","text":"","code":"# orsf_control_net() is considerably slower than orsf_control_cph(), # The example uses n_tree = 25 so that my examples run faster, # but you should use at least 500 trees in applied settings. orsf(data = pbc_orsf, formula = Surv(time, status) ~ . - id, n_tree = 25, control = orsf_control_net()) #> ---------- Oblique random survival forest #> #> Linear combinations: Penalized Cox regression #> N observations: 276 #> N events: 111 #> N trees: 25 #> N predictors total: 17 #> N predictors per node: 5 #> Average leaves per tree: 24 #> Min observations in leaf: 5 #> Min events in leaf: 1 #> OOB stat value: 0.83 #> OOB stat type: Harrell's C-statistic #> Variable importance: anova #> #> -----------------------------------------"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_ice_oob.html","id":null,"dir":"Reference","previous_headings":"","what":"ORSF Individual Conditional Expectations — orsf_ice_oob","title":"ORSF Individual Conditional Expectations — orsf_ice_oob","text":"Compute individual conditional expectations ORSF model. Unlike partial dependence, shows expected prediction function one multiple predictors, individual conditional expectations (ICE) show prediction individual observation function predictor. can compute individual conditional expectations three ways using random forest: using -bag predictions training data using --bag predictions training data using predictions new set data See examples details","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_ice_oob.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ORSF Individual Conditional Expectations — orsf_ice_oob","text":"","code":"orsf_ice_oob( object, pred_spec, pred_horizon = NULL, pred_type = \"risk\", expand_grid = TRUE, boundary_checks = TRUE, n_thread = 1, ... ) orsf_ice_inb( object, pred_spec, pred_horizon = NULL, pred_type = \"risk\", expand_grid = TRUE, boundary_checks = TRUE, n_thread = 1, ... ) orsf_ice_new( object, pred_spec, new_data, pred_horizon = NULL, pred_type = \"risk\", na_action = \"fail\", expand_grid = TRUE, boundary_checks = TRUE, n_thread = 1, ... )"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_ice_oob.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ORSF Individual Conditional Expectations — orsf_ice_oob","text":"object (orsf_fit) trained oblique random survival forest (see orsf). pred_spec (named list data.frame). pred_spec named list, item list vector values used points partial dependence function. name item list indicate variable modified take corresponding values. pred_spec data.frame, columns indicate variable names, values indicate variable values, partial dependence computed using inputs row. pred_horizon (double) value vector indicating time(s) predictions calibrated . E.g., predicting risk incident heart failure within next 10 years, pred_horizon = 10. pred_horizon can NULL pred_type 'mort', since mortality predictions aggregated event times pred_type (character) type predictions compute. Valid options 'risk' : probability event pred_horizon. 'surv' : 1 - risk. 'chf': cumulative hazard function 'mort': mortality prediction expand_grid (logical) TRUE, partial dependence computed possible combinations inputs pred_spec. FALSE, partial dependence computed variable pred_spec, separately. boundary_checks (logical) TRUE, pred_spec checked make sure requested values 10th 90th percentile object's training data. FALSE, checks skipped. n_thread (integer) number threads use computing predictions. Default one thread. use maximum number threads system provides concurrent execution, set n_thread = 0. ... arguments passed methods (currently used). new_data data.frame, tibble, data.table compute predictions . na_action (character) happen new_data contains missing values (.e., NA values). Valid options : 'fail' : error thrown new_data contains NA values 'omit' : rows new_data incomplete data dropped","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_ice_oob.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ORSF Individual Conditional Expectations — orsf_ice_oob","text":"data.table containing individual conditional expectations specified variable(s) specified prediction horizon(s).","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_ice_oob.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ORSF Individual Conditional Expectations — orsf_ice_oob","text":"Begin fitting ORSF ensemble Use ensemble compute ICE values using --bag predictions: Much detailed examples given vignette","code":"library(aorsf) set.seed(329) fit <- orsf(data = pbc_orsf, formula = Surv(time, status) ~ . - id) fit ## ---------- Oblique random survival forest ## ## Linear combinations: Accelerated ## N observations: 276 ## N events: 111 ## N trees: 500 ## N predictors total: 17 ## N predictors per node: 5 ## Average leaves per tree: 25 ## Min observations in leaf: 5 ## Min events in leaf: 1 ## OOB stat value: 0.84 ## OOB stat type: Harrell's C-statistic ## Variable importance: anova ## ## ----------------------------------------- pred_spec <- list(bili = seq(1, 10, length.out = 25)) ice_oob <- orsf_ice_oob(fit, pred_spec, boundary_checks = FALSE) ice_oob ## id_variable id_row pred_horizon bili pred ## 1: 1 1 1788 1 0.9011797 ## 2: 1 2 1788 1 0.1096207 ## 3: 1 3 1788 1 0.7646444 ## 4: 1 4 1788 1 0.3531060 ## 5: 1 5 1788 1 0.1228441 ## --- ## 6896: 25 272 1788 10 0.3089586 ## 6897: 25 273 1788 10 0.4005430 ## 6898: 25 274 1788 10 0.4933945 ## 6899: 25 275 1788 10 0.3134373 ## 6900: 25 276 1788 10 0.5002014"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_pd_oob.html","id":null,"dir":"Reference","previous_headings":"","what":"ORSF partial dependence — orsf_pd_oob","title":"ORSF partial dependence — orsf_pd_oob","text":"Compute partial dependence ORSF model. Partial dependence (PD) shows expected prediction model function single predictor multiple predictors. expectation marginalized values predictors, giving something like multivariable adjusted estimate model's prediction. can compute partial dependence three ways using random forest: using -bag predictions training data using --bag predictions training data using predictions new set data See examples details","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_pd_oob.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ORSF partial dependence — orsf_pd_oob","text":"","code":"orsf_pd_oob( object, pred_spec, pred_horizon = NULL, pred_type = \"risk\", expand_grid = TRUE, prob_values = c(0.025, 0.5, 0.975), prob_labels = c(\"lwr\", \"medn\", \"upr\"), boundary_checks = TRUE, n_thread = 1, ... ) orsf_pd_inb( object, pred_spec, pred_horizon = NULL, pred_type = \"risk\", expand_grid = TRUE, prob_values = c(0.025, 0.5, 0.975), prob_labels = c(\"lwr\", \"medn\", \"upr\"), boundary_checks = TRUE, n_thread = 1, ... ) orsf_pd_new( object, pred_spec, new_data, pred_horizon = NULL, pred_type = \"risk\", na_action = \"fail\", expand_grid = TRUE, prob_values = c(0.025, 0.5, 0.975), prob_labels = c(\"lwr\", \"medn\", \"upr\"), boundary_checks = TRUE, n_thread = 1, ... )"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_pd_oob.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ORSF partial dependence — orsf_pd_oob","text":"object (orsf_fit) trained oblique random survival forest (see orsf). pred_spec (named list data.frame). pred_spec named list, item list vector values used points partial dependence function. name item list indicate variable modified take corresponding values. pred_spec data.frame, columns indicate variable names, values indicate variable values, partial dependence computed using inputs row. pred_horizon (double) value vector indicating time(s) predictions calibrated . E.g., predicting risk incident heart failure within next 10 years, pred_horizon = 10. pred_horizon can NULL pred_type 'mort', since mortality predictions aggregated event times pred_type (character) type predictions compute. Valid options 'risk' : probability event pred_horizon. 'surv' : 1 - risk. 'chf': cumulative hazard function 'mort': mortality prediction expand_grid (logical) TRUE, partial dependence computed possible combinations inputs pred_spec. FALSE, partial dependence computed variable pred_spec, separately. prob_values (numeric) vector values 0 1, indicating quantiles used summarize partial dependence values set inputs. prob_values length prob_labels. quantiles calculated based predictions object set values indicated pred_spec. prob_labels (character) vector labels length prob_values, label indicating corresponding value prob_values labelled summarized outputs. prob_labels length prob_values. boundary_checks (logical) TRUE, pred_spec checked make sure requested values 10th 90th percentile object's training data. FALSE, checks skipped. n_thread (integer) number threads use computing predictions. Default one thread. use maximum number threads system provides concurrent execution, set n_thread = 0. ... arguments passed methods (currently used). new_data data.frame, tibble, data.table compute predictions . na_action (character) happen new_data contains missing values (.e., NA values). Valid options : 'fail' : error thrown new_data contains NA values 'omit' : rows new_data incomplete data dropped","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_pd_oob.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ORSF partial dependence — orsf_pd_oob","text":"data.table containing partial dependence values specified variable(s) specified prediction horizon(s).","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_pd_oob.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ORSF partial dependence — orsf_pd_oob","text":"Partial dependence number known limitations assumptions users aware (see Hooker, 2021). particular, partial dependence less intuitive >2 predictors examined jointly, assumed feature(s) partial dependence computed correlated features (likely true many cases). Accumulated local effect plots can used (see ) case feature independence valid assumption.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_pd_oob.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ORSF partial dependence — orsf_pd_oob","text":"Begin fitting ORSF ensemble:","code":"library(aorsf) set.seed(329730) index_train <- sample(nrow(pbc_orsf), 150) pbc_orsf_train <- pbc_orsf[index_train, ] pbc_orsf_test <- pbc_orsf[-index_train, ] fit <- orsf(data = pbc_orsf_train, formula = Surv(time, status) ~ . - id, oobag_pred_horizon = 365.25 * 5)"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_pd_oob.html","id":"three-ways-to-compute-pd-and-ice","dir":"Reference","previous_headings":"","what":"Three ways to compute PD and ICE","title":"ORSF partial dependence — orsf_pd_oob","text":"can compute partial dependence ICE three ways aorsf: using -bag predictions training data using --bag predictions training data using predictions new set data -bag partial dependence indicates relationships model learned training. helpful goal interpret model. --bag partial dependence indicates relationships model learned training using --bag data simulates application model new data. want test model’s reliability fairness new data don’t access large testing set. new data partial dependence shows model predicts outcomes observations seen. helpful want test model’s reliability fairness.","code":"pd_train <- orsf_pd_inb(fit, pred_spec = list(bili = 1:5)) pd_train ## pred_horizon bili mean lwr medn upr ## 1: 1826.25 1 0.2151663 0.02028479 0.09634648 0.7997269 ## 2: 1826.25 2 0.2576618 0.03766695 0.15497447 0.8211875 ## 3: 1826.25 3 0.2998484 0.06436773 0.20771324 0.8425637 ## 4: 1826.25 4 0.3390664 0.08427149 0.25401067 0.8589590 ## 5: 1826.25 5 0.3699045 0.10650098 0.28284427 0.8689855 pd_train <- orsf_pd_oob(fit, pred_spec = list(bili = 1:5)) pd_train ## pred_horizon bili mean lwr medn upr ## 1: 1826.25 1 0.2145044 0.01835000 0.09619052 0.7980629 ## 2: 1826.25 2 0.2566241 0.03535358 0.14185734 0.8173143 ## 3: 1826.25 3 0.2984693 0.05900059 0.20515477 0.8334243 ## 4: 1826.25 4 0.3383547 0.07887323 0.24347513 0.8469769 ## 5: 1826.25 5 0.3696260 0.10450534 0.28065473 0.8523756 pd_test <- orsf_pd_new(fit, new_data = pbc_orsf_test, pred_spec = list(bili = 1:5)) pd_test ## pred_horizon bili mean lwr medn upr ## 1: 1826.25 1 0.2542230 0.02901386 0.1943767 0.8143912 ## 2: 1826.25 2 0.2955726 0.05037316 0.2474559 0.8317684 ## 3: 1826.25 3 0.3388434 0.07453896 0.3010898 0.8488622 ## 4: 1826.25 4 0.3800254 0.10565022 0.3516805 0.8592057 ## 5: 1826.25 5 0.4124587 0.12292465 0.3915066 0.8690074"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_pd_oob.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"ORSF partial dependence — orsf_pd_oob","text":"Giles Hooker, Lucas Mentch, Siyu Zhou. Unrestricted Permutation forces Extrapolation: Variable Importance Requires least One Model, Free Variable Importance. arXiv e-prints 2021 Oct; arXiv-1905. URL: https://doi.org/10.48550/arXiv.1905.03151","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_scale_cph.html","id":null,"dir":"Reference","previous_headings":"","what":"Scale input data — orsf_scale_cph","title":"Scale input data — orsf_scale_cph","text":"functions exported users may access internal routines used scale inputs orsf_control_cph used.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_scale_cph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scale input data — orsf_scale_cph","text":"","code":"orsf_scale_cph(x_mat, w_vec = NULL) orsf_unscale_cph(x_mat)"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_scale_cph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scale input data — orsf_scale_cph","text":"x_mat (numeric matrix) matrix values scaled unscaled. Note orsf_unscale_cph accept x_mat inputs attribute containing transform values, added automatically orsf_scale_cph. w_vec (numeric vector) optional vector weights. weights supplied (default), observations equally weighted. supplied, w_vec must length equal nrow(x_mat).","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_scale_cph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scale input data — orsf_scale_cph","text":"scaled unscaled x_mat.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_scale_cph.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Scale input data — orsf_scale_cph","text":"data transformed first subtracting mean multiplying scale. inverse transform can completed using orsf_unscale_cph dividing column corresponding scale adding mean. values means scales stored attribute output returned orsf_scale_cph (see examples)","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_scale_cph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scale input data — orsf_scale_cph","text":"","code":"x_mat <- as.matrix(pbc_orsf[, c('bili', 'age', 'protime')]) head(x_mat) #> bili age protime #> 1 14.5 58.76523 12.2 #> 2 1.1 56.44627 10.6 #> 3 1.4 70.07255 12.0 #> 4 1.8 54.74059 10.3 #> 5 3.4 38.10541 10.9 #> 7 1.0 55.53457 9.7 x_scaled <- orsf_scale_cph(x_mat) head(x_scaled) #> bili age protime #> [1,] 3.77308887 1.0412574 1.9694656 #> [2,] -0.75476469 0.7719344 -0.1822316 #> [3,] -0.65339483 2.3544852 1.7005035 #> [4,] -0.51823502 0.5738373 -0.5856748 #> [5,] 0.02240421 -1.3581657 0.2212116 #> [6,] -0.78855464 0.6660494 -1.3925613 attributes(x_scaled) # note the transforms attribute #> $dim #> [1] 276 3 #> #> $dimnames #> $dimnames[[1]] #> NULL #> #> $dimnames[[2]] #> [1] \"bili\" \"age\" \"protime\" #> #> #> $transforms #> mean scale #> [1,] 3.333696 0.3378995 #> [2,] 49.799661 0.1161396 #> [3,] 10.735507 1.3448108 #> x_unscaled <- orsf_unscale_cph(x_scaled) head(x_unscaled) #> bili age protime #> [1,] 14.5 58.76523 12.2 #> [2,] 1.1 56.44627 10.6 #> [3,] 1.4 70.07255 12.0 #> [4,] 1.8 54.74059 10.3 #> [5,] 3.4 38.10541 10.9 #> [6,] 1.0 55.53457 9.7 # numeric difference in x_mat and x_unscaled should be practically 0 max(abs(x_mat - x_unscaled)) #> [1] 3.552714e-15"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_summarize_uni.html","id":null,"dir":"Reference","previous_headings":"","what":"ORSF summary; univariate — orsf_summarize_uni","title":"ORSF summary; univariate — orsf_summarize_uni","text":"Summarize univariate information ORSF object","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_summarize_uni.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ORSF summary; univariate — orsf_summarize_uni","text":"","code":"orsf_summarize_uni( object, n_variables = NULL, pred_horizon = NULL, pred_type = \"risk\", importance = \"negate\", ... )"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_summarize_uni.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ORSF summary; univariate — orsf_summarize_uni","text":"object (orsf_fit) trained oblique random survival forest (see orsf). n_variables (integer) many variables summarized? Setting input lower number reduce computation time. pred_horizon (double) value vector indicating time(s) predictions calibrated . E.g., predicting risk incident heart failure within next 10 years, pred_horizon = 10. pred_horizon can NULL pred_type 'mort', since mortality predictions aggregated event times pred_type (character) type predictions compute. Valid options 'risk' : probability event pred_horizon. 'surv' : 1 - risk. 'chf': cumulative hazard function 'mort': mortality prediction importance (character) Indicate method variable importance: 'none': variable importance computed. 'anova': compute analysis variance (ANOVA) importance 'negate': compute negation importance 'permute': compute permutation importance details methods, see orsf_vi. ... arguments passed methods (currently used).","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_summarize_uni.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ORSF summary; univariate — orsf_summarize_uni","text":"object class 'orsf_summary', includes data importance individual predictors. expected values predictions specific values predictors.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_summarize_uni.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ORSF summary; univariate — orsf_summarize_uni","text":"pred_horizon left unspecified, median value time--event variable object's training data used. recommended always specify prediction horizon, median time may especially meaningful horizon compute predicted risk values . object already variable importance values, can safely bypass computation variable importance function setting importance = 'none'.","code":""},{"path":[]},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_summarize_uni.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ORSF summary; univariate — orsf_summarize_uni","text":"","code":"object <- orsf(pbc_orsf, Surv(time, status) ~ . - id) # since anova importance was used to make object, we can # safely say importance = 'none' and skip computation of # variable importance while running orsf_summarize_uni orsf_summarize_uni(object, n_variables = 3, importance = 'none') #> #> -- ascites (VI Rank: 1) ------------------------ #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 0 0.3056310 0.1674688 0.05252807 0.5526941 #> 1 0.4442562 0.3536698 0.22121860 0.6347482 #> #> -- edema (VI Rank: 2) -------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 0 0.2995075 0.1657309 0.05169169 0.5528988 #> 0.5 0.3625881 0.2581942 0.10846891 0.6305115 #> 1 0.4564929 0.3715971 0.24401693 0.6522910 #> #> -- bili (VI Rank: 3) --------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 0.80 0.2401344 0.1335075 0.05493055 0.3787130 #> 1.4 0.2665132 0.1618601 0.06895290 0.4172400 #> 3.5 0.3704224 0.2788340 0.15248087 0.5542718 #> #> Predicted risk at time t = 1788 for top 3 predictors # however, if we want to summarize object according to variables # ranked by negation importance, we can compute negation importance # within orsf_summarize_uni() as follows: orsf_summarize_uni(object, n_variables = 3, importance = 'negate') #> #> -- bili (VI Rank: 1) --------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 0.80 0.2401344 0.1335075 0.05493055 0.3787130 #> 1.4 0.2665132 0.1618601 0.06895290 0.4172400 #> 3.5 0.3704224 0.2788340 0.15248087 0.5542718 #> #> -- copper (VI Rank: 2) ------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 43 0.2698245 0.1495333 0.05165107 0.4526158 #> 74 0.2958194 0.1690855 0.07162885 0.4905227 #> 129 0.3513684 0.2418974 0.12093492 0.5513119 #> #> -- sex (VI Rank: 3) ---------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> m 0.3606311 0.2502953 0.11850705 0.5863728 #> f 0.3041468 0.1632798 0.05252807 0.5341361 #> #> Predicted risk at time t = 1788 for top 3 predictors"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_time_to_train.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate training time — orsf_time_to_train","title":"Estimate training time — orsf_time_to_train","text":"Estimate training time","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_time_to_train.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate training time — orsf_time_to_train","text":"","code":"orsf_time_to_train(object, n_tree_subset = 50)"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_time_to_train.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate training time — orsf_time_to_train","text":"object untrained aorsf object n_tree_subset (integer) many trees fit order estimate time needed train object. default value 50, usually gives good enough approximation.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_time_to_train.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate training time — orsf_time_to_train","text":"difftime object.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_time_to_train.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate training time — orsf_time_to_train","text":"","code":"# specify but do not train the model by setting no_fit = TRUE. object <- orsf(pbc_orsf, Surv(time, status) ~ . - id, n_tree = 500, no_fit = TRUE) # grow 50 trees to approximate the time it will take to grow 500 trees time_estimated <- orsf_time_to_train(object, n_tree_subset = 50) print(time_estimated) #> Time difference of 0.2824929 secs # let's see how close the approximation was time_true_start <- Sys.time() fit <- orsf_train(object) time_true_stop <- Sys.time() time_true <- time_true_stop - time_true_start print(time_true) #> Time difference of 0.2648766 secs # error abs(time_true - time_estimated) #> Time difference of 0.01761627 secs"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vi.html","id":null,"dir":"Reference","previous_headings":"","what":"ORSF variable importance — orsf_vi","title":"ORSF variable importance — orsf_vi","text":"Estimate importance individual variables using oblique random survival forests.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ORSF variable importance — orsf_vi","text":"","code":"orsf_vi( object, group_factors = TRUE, importance = NULL, oobag_fun = NULL, n_thread = 1, verbose_progress = FALSE, ... ) orsf_vi_negate( object, group_factors = TRUE, oobag_fun = NULL, n_thread = 1, verbose_progress = FALSE, ... ) orsf_vi_permute( object, group_factors = TRUE, oobag_fun = NULL, n_thread = 1, verbose_progress = FALSE, ... ) orsf_vi_anova(object, group_factors = TRUE, ...)"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ORSF variable importance — orsf_vi","text":"object (orsf_fit) trained oblique random survival forest (see orsf). group_factors (logical) TRUE, importance factor variables reported overall aggregating importance individual levels factor. FALSE, importance individual factor levels returned. importance (character) Indicate method variable importance: 'anova': compute analysis variance (ANOVA) importance 'negate': compute negation importance 'permute': compute permutation importance oobag_fun (function) used evaluating --bag prediction accuracy negating coefficients (importance = 'negate') permuting values predictor (importance = 'permute') oobag_fun = NULL (default), Harrell's C-statistic (1982) used evaluate accuracy. use oobag_fun note following: oobag_fun two inputs: y_mat s_vec y_mat two column matrix first column named 'time', second named 'status' s_vec numeric vector containing predicted survival probabilities. oobag_fun return numeric output length 1 oobag_fun used created object initial value --bag prediction accuracy consistent values computed variable importance estimated. details, see --bag vignette. n_thread (integer) number threads use computing predictions. Default one thread. use maximum number threads system provides concurrent execution, set n_thread = 0. verbose_progress (logical) TRUE, progress messages printed console. FALSE (default), nothing printed. ... arguments passed methods (currently used).","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ORSF variable importance — orsf_vi","text":"orsf_vi functions return named numeric vector. Names vector predictor variables used object Values vector estimated importance given predictor. returned vector sorted highest lowest value, higher values indicating higher importance.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ORSF variable importance — orsf_vi","text":"orsf_fit object fitted importance = 'anova', 'negate', 'permute', output vector importance values based requested type importance. However, may still want call orsf_vi() output want group factor levels one overall importance value. orsf_vi() general purpose function extract compute variable importance estimates 'orsf_fit' object (see orsf). orsf_vi_negate(), orsf_vi_permute(), orsf_vi_anova() wrappers orsf_vi(). way functions work depends whether object given already variable importance estimates (see examples).","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vi.html","id":"variable-importance-methods","dir":"Reference","previous_headings":"","what":"Variable importance methods","title":"ORSF variable importance — orsf_vi","text":"negation importance: variable assessed separately multiplying variable's coefficients -1 determining much model's performance changes. worse model's performance negating coefficients given variable, important variable. technique promising b/c require permutation emphasizes variables larger coefficients linear combinations, also relatively new studied much permutation importance. See Jaeger, 2022 details technique. permutation importance: variable assessed separately randomly permuting variable's values determining much model's performance changes. worse model's performance permuting values given variable, important variable. technique flexible, intuitive, frequently used. also several known limitations analysis variance (ANOVA) importance: p-value computed coefficient linear combination variables decision tree. Importance individual predictor variable proportion times p-value coefficient < 0.01. technique efficient computationally, may effective permutation negation terms selecting signal noise variables. See Menze, 2011 details technique.","code":""},{"path":[]},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vi.html","id":"anova-importance","dir":"Reference","previous_headings":"","what":"ANOVA importance","title":"ORSF variable importance — orsf_vi","text":"default variable importance technique, ANOVA, calculated fit ORSF ensemble. ANOVA default fast, may decisive permutation negation techniques variable selection.","code":"fit <- orsf(pbc_orsf, Surv(time, status) ~ . - id) fit ## ---------- Oblique random survival forest ## ## Linear combinations: Accelerated ## N observations: 276 ## N events: 111 ## N trees: 500 ## N predictors total: 17 ## N predictors per node: 5 ## Average leaves per tree: 25 ## Min observations in leaf: 5 ## Min events in leaf: 1 ## OOB stat value: 0.84 ## OOB stat type: Harrell's C-statistic ## Variable importance: anova ## ## -----------------------------------------"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vi.html","id":"raw-vi-values","dir":"Reference","previous_headings":"","what":"Raw VI values","title":"ORSF variable importance — orsf_vi","text":"‘raw’ variable importance values can accessed fit object ‘raw’ values factors aggregated single value. Currently one value k-1 levels k level factor. example, can see edema_1 edema_0.5 importance values edema factor variable levels 0, 0.5, 1.","code":"attr(fit, 'importance_values') ## ascites_1 edema_1 bili albumin copper edema_0.5 ## 0.44146501 0.43190921 0.29391304 0.22145499 0.22120519 0.20110957 ## age protime chol spiders_1 stage sex_f ## 0.19980193 0.19329637 0.17777778 0.17772293 0.16048729 0.15926709 ## hepato_1 ast trig alk.phos platelet trt_placebo ## 0.15816481 0.15734785 0.13200993 0.12433796 0.11844461 0.09404636"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vi.html","id":"collapse-vi-across-factor-levels","dir":"Reference","previous_headings":"","what":"Collapse VI across factor levels","title":"ORSF variable importance — orsf_vi","text":"get aggregated values across levels factor, access importance element orsf fit: use orsf_vi() group_factors set TRUE (default) Note can make default returned importance values ungrouped setting group_factors FALSE orsf_vi functions orsf function.","code":"fit$importance ## ascites edema bili albumin copper age protime ## 0.44146501 0.29452847 0.29391304 0.22145499 0.22120519 0.19980193 0.19329637 ## chol spiders stage sex hepato ast trig ## 0.17777778 0.17772293 0.16048729 0.15926709 0.15816481 0.15734785 0.13200993 ## alk.phos platelet trt ## 0.12433796 0.11844461 0.09404636 orsf_vi(fit) ## ascites edema bili albumin copper age protime ## 0.44146501 0.29452847 0.29391304 0.22145499 0.22120519 0.19980193 0.19329637 ## chol spiders stage sex hepato ast trig ## 0.17777778 0.17772293 0.16048729 0.15926709 0.15816481 0.15734785 0.13200993 ## alk.phos platelet trt ## 0.12433796 0.11844461 0.09404636"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vi.html","id":"add-vi-to-an-orsf","dir":"Reference","previous_headings":"","what":"Add VI to an ORSF","title":"ORSF variable importance — orsf_vi","text":"can fit ORSF without VI, add VI later","code":"fit_no_vi <- orsf(pbc_orsf, Surv(time, status) ~ . - id, importance = 'none') # Note: you can't call orsf_vi_anova() on fit_no_vi because anova # VI can only be computed while the forest is being grown. orsf_vi_negate(fit_no_vi) ## bili copper sex protime stage ## 0.1139657923 0.0498712200 0.0355366377 0.0283554322 0.0263792287 ## albumin age ascites chol ast ## 0.0231636378 0.0195791833 0.0175120075 0.0148252414 0.0104918262 ## edema spiders hepato trt trig ## 0.0084871358 0.0070608860 0.0067054788 0.0052040792 0.0030363455 ## alk.phos platelet ## 0.0029918139 -0.0003309069 orsf_vi_permute(fit_no_vi) ## bili copper protime albumin stage ## 0.0511641625 0.0244676999 0.0160869571 0.0133334120 0.0130092352 ## ascites age hepato edema chol ## 0.0127421184 0.0113532728 0.0050851329 0.0050477457 0.0049382275 ## ast spiders sex alk.phos trig ## 0.0047345189 0.0038719163 0.0025231267 0.0018408350 0.0011528848 ## platelet trt ## -0.0002875319 -0.0024330707"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vi.html","id":"orsf-and-vi-all-at-once","dir":"Reference","previous_headings":"","what":"ORSF and VI all at once","title":"ORSF variable importance — orsf_vi","text":"fit ORSF compute vi time can still get negation VI fit, needs computed","code":"fit_permute_vi <- orsf(pbc_orsf, Surv(time, status) ~ . - id, importance = 'permute') # get the vi instantly (i.e., it doesn't need to be computed again) orsf_vi_permute(fit_permute_vi) ## bili copper age albumin protime ## 0.0502725526 0.0201473283 0.0135888938 0.0127241082 0.0126629150 ## stage ascites ast edema chol ## 0.0124866976 0.0123508555 0.0060741690 0.0059166139 0.0053767371 ## spiders sex hepato trig alk.phos ## 0.0042600602 0.0028177750 0.0023470782 0.0021331719 0.0016874102 ## platelet trt ## 0.0002117061 -0.0005790547 orsf_vi_negate(fit_permute_vi) ## bili copper sex stage age protime ## 0.1106715167 0.0456031656 0.0306666098 0.0304383573 0.0252136203 0.0224838590 ## albumin ascites chol ast edema trt ## 0.0212630703 0.0168893963 0.0134174671 0.0132075752 0.0099681058 0.0088378768 ## spiders hepato trig alk.phos platelet ## 0.0078776082 0.0062877323 0.0043076141 0.0030432581 0.0005571111"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vi.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"ORSF variable importance — orsf_vi","text":"Harrell FE, Califf RM, Pryor DB, Lee KL, Rosati RA. Evaluating Yield Medical Tests. JAMA 1982; 247(18):2543-2546. DOI: 10.1001/jama.1982.03320430047030 Breiman L. Random forests. Machine learning 2001 Oct; 45(1):5-32. DOI: 10.1023/:1010933404324 Menze BH, Kelm BM, Splitthoff DN, Koethe U, Hamprecht FA. oblique random forests. Joint European Conference Machine Learning Knowledge Discovery Databases 2011 Sep 4; pp. 453-469. DOI: 10.1007/978-3-642-23783-6_29 Jaeger BC, Welden S, Lenoir K, Speiser JL, Segar MW, Pandey , Pajewski NM. Accelerated interpretable oblique random survival forests. arXiv e-prints 2022 Aug; arXiv-2208. URL: https://arxiv.org/abs/2208.01129","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vs.html","id":null,"dir":"Reference","previous_headings":"","what":"Variable selection — orsf_vs","title":"Variable selection — orsf_vs","text":"Variable selection","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variable selection — orsf_vs","text":"","code":"orsf_vs(object, n_predictor_min = 3, verbose_progress = FALSE)"},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variable selection — orsf_vs","text":"object (orsf_fit) trained oblique random survival forest (see orsf). n_predictor_min (integer) minimum number predictors allowed verbose_progress (logical) implemented yet. progress printed console?","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Variable selection — orsf_vs","text":"data.table four columns: n_predictors: number predictors used stat_value: --bag statistic predictors_included: names predictors included predictor_dropped: predictor selected dropped","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Variable selection — orsf_vs","text":"tree_seeds specified object successive run orsf evaluated --bag samples initial run.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/orsf_vs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Variable selection — orsf_vs","text":"","code":"object <- orsf(formula = time + status ~ ., data = pbc_orsf, n_tree = 25, importance = 'anova', tree_seeds = 1:25) orsf_vs(object) #> n_predictors stat_value predictors_included #> 1: 3 0.7937393 ascites,edema,bili #> 2: 4 0.8159279 age,ascites,edema,bili #> 3: 5 0.8205115 age,ascites,edema,bili,copper #> 4: 6 0.8316839 age,ascites,edema,bili,albumin,copper #> 5: 7 0.8346008 age,ascites,edema,bili,chol,albumin,... #> 6: 8 0.8277254 age,ascites,edema,bili,chol,albumin,... #> 7: 9 0.8322048 age,ascites,edema,bili,chol,albumin,... #> 8: 10 0.8252253 age,ascites,edema,bili,chol,albumin,... #> 9: 11 0.8277775 age,sex,ascites,edema,bili,chol,... #> 10: 12 0.8199385 age,sex,ascites,hepato,edema,bili,... #> 11: 13 0.8214490 age,sex,ascites,hepato,spiders,edema,... #> 12: 14 0.8298088 age,sex,ascites,hepato,spiders,edema,... #> 13: 15 0.8151206 age,sex,ascites,hepato,spiders,edema,... #> 14: 16 0.8391062 age,sex,ascites,hepato,spiders,edema,... #> 15: 17 0.8200167 id,age,sex,ascites,hepato,spiders,... #> 16: 18 0.8105370 id,trt,age,sex,ascites,hepato,... #> predictor_dropped #> 1: ascites #> 2: age #> 3: copper #> 4: albumin #> 5: chol #> 6: protime #> 7: ast #> 8: alk.phos #> 9: sex #> 10: hepato #> 11: spiders #> 12: stage #> 13: platelet #> 14: trig #> 15: id #> 16: trt"},{"path":"https://bcjaeger.github.io/aorsf/reference/pbc_orsf.html","id":null,"dir":"Reference","previous_headings":"","what":"Mayo Clinic Primary Biliary Cholangitis Data — pbc_orsf","title":"Mayo Clinic Primary Biliary Cholangitis Data — pbc_orsf","text":"data light modification survival::pbc data. modifications :","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/pbc_orsf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mayo Clinic Primary Biliary Cholangitis Data — pbc_orsf","text":"","code":"pbc_orsf"},{"path":"https://bcjaeger.github.io/aorsf/reference/pbc_orsf.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Mayo Clinic Primary Biliary Cholangitis Data — pbc_orsf","text":"data frame 276 rows 20 variables: id case number time number days registration earlier death, transplantion, study analysis July, 1986 status status endpoint, 0 censored transplant, 1 dead trt randomized treatment group: D-penicillmain placebo age years sex m/f ascites presence ascites hepato presence hepatomegaly enlarged liver spiders blood vessel malformations skin edema 0 edema, 0.5 untreated successfully treated, 1 edema despite diuretic therapy bili serum bilirubin (mg/dl) chol serum cholesterol (mg/dl) albumin serum albumin (g/dl) copper urine copper (ug/day) alk.phos alkaline phosphotase (U/liter) ast aspartate aminotransferase, called SGOT (U/ml) trig triglycerides (mg/dl) platelet platelet count protime standardized blood clotting time stage histologic stage disease (needs biopsy)","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/pbc_orsf.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Mayo Clinic Primary Biliary Cholangitis Data — pbc_orsf","text":"T Therneau P Grambsch (2000), Modeling Survival Data: Extending Cox Model, Springer-Verlag, New York. ISBN: 0-387-98784-3.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/pbc_orsf.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Mayo Clinic Primary Biliary Cholangitis Data — pbc_orsf","text":"removed rows missing data converted status 0 censor transplant, 1 dead converted stage ordered factor. converted trt, ascites, hepato, spiders, edema factors.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/predict.orsf_fit.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute predictions using ORSF — predict.orsf_fit","title":"Compute predictions using ORSF — predict.orsf_fit","text":"Predicted risk, survival, hazard, mortality ORSF model.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/predict.orsf_fit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute predictions using ORSF — predict.orsf_fit","text":"","code":"# S3 method for orsf_fit predict( object, new_data, pred_horizon = NULL, pred_type = \"risk\", na_action = \"fail\", boundary_checks = TRUE, n_thread = 1, verbose_progress = FALSE, pred_aggregate = TRUE, ... )"},{"path":"https://bcjaeger.github.io/aorsf/reference/predict.orsf_fit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute predictions using ORSF — predict.orsf_fit","text":"object (orsf_fit) trained oblique random survival forest (see orsf). new_data data.frame, tibble, data.table compute predictions . pred_horizon (double) value vector indicating time(s) predictions calibrated . E.g., predicting risk incident heart failure within next 10 years, pred_horizon = 10. pred_horizon can NULL pred_type 'mort', since mortality predictions aggregated event times pred_type (character) type predictions compute. Valid options 'risk' : probability event pred_horizon. 'surv' : 1 - risk. 'chf': cumulative hazard function 'mort': mortality prediction na_action (character) happen new_data contains missing values (.e., NA values). Valid options : 'fail' : error thrown new_data contains NA values 'pass' : output NA rows new_data 1 NA value predictors used object 'omit' : rows new_data incomplete data dropped 'impute_meanmode' : missing values continuous categorical variables new_data imputed using mean mode, respectively. clarify, mean mode used impute missing values training data object, new_data. boundary_checks (logical) TRUE, pred_horizon checked make sure requested values less maximum observed time object's training data. FALSE, checks skipped. n_thread (integer) number threads use computing predictions. Default one thread. use maximum number threads system provides concurrent execution, set n_thread = 0. verbose_progress (logical) TRUE, progress messages printed console. FALSE (default), nothing printed. pred_aggregate (logical) TRUE (default), predictions aggregated trees taking mean. FALSE, returned output contain one row per observation one column tree. length pred_horizon two pred_aggregate FALSE, result list matrices, 'th item list corresponding 'th value pred_horizon. ... arguments passed methods (currently used).","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/predict.orsf_fit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute predictions using ORSF — predict.orsf_fit","text":"matrix predictions. Column j matrix corresponds value j pred_horizon. Row matrix corresponds row new_data.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/predict.orsf_fit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute predictions using ORSF — predict.orsf_fit","text":"new_data must columns equivalent types data used train object. Also, factors new_data must levels data used train object. pred_horizon values exceed maximum follow-time object's training data, truly want , set boundary_checks = FALSE can use pred_horizon large want. Note predictions beyond maximum follow-time object's training data equal predictions maximum follow-time, aorsf estimate survival beyond maximum observed time. unspecified, pred_horizon may automatically specified value used oobag_pred_horizon object created (see orsf).","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/predict.orsf_fit.html","id":"examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute predictions using ORSF — predict.orsf_fit","text":"Begin fitting ORSF ensemble: Predict risk, survival, cumulative hazard one several times: Predict mortality, defined number events forest’s population observations characteristics like current observation. type prediction require specify prediction horizon","code":"library(aorsf) set.seed(329730) index_train <- sample(nrow(pbc_orsf), 150) pbc_orsf_train <- pbc_orsf[index_train, ] pbc_orsf_test <- pbc_orsf[-index_train, ] fit <- orsf(data = pbc_orsf_train, formula = Surv(time, status) ~ . - id, oobag_pred_horizon = 365.25 * 5) # predicted risk, the default predict(fit, new_data = pbc_orsf_test[1:5, ], pred_type = 'risk', pred_horizon = c(500, 1000, 1500)) ## [,1] [,2] [,3] ## [1,] 0.49679905 0.77309053 0.90830168 ## [2,] 0.03363621 0.08527972 0.17061414 ## [3,] 0.15129784 0.30402666 0.43747212 ## [4,] 0.01152480 0.02950914 0.07068198 ## [5,] 0.01035341 0.01942262 0.05117679 # predicted survival, i.e., 1 - risk predict(fit, new_data = pbc_orsf_test[1:5, ], pred_type = 'surv', pred_horizon = c(500, 1000, 1500)) ## [,1] [,2] [,3] ## [1,] 0.5032009 0.2269095 0.09169832 ## [2,] 0.9663638 0.9147203 0.82938586 ## [3,] 0.8487022 0.6959733 0.56252788 ## [4,] 0.9884752 0.9704909 0.92931802 ## [5,] 0.9896466 0.9805774 0.94882321 # predicted cumulative hazard function # (expected number of events for person i at time j) predict(fit, new_data = pbc_orsf_test[1:5, ], pred_type = 'chf', pred_horizon = c(500, 1000, 1500)) ## [,1] [,2] [,3] ## [1,] 0.74442414 1.39538511 1.78344589 ## [2,] 0.03473938 0.10418984 0.24047328 ## [3,] 0.19732086 0.47015754 0.73629459 ## [4,] 0.01169147 0.03223257 0.09564168 ## [5,] 0.01072007 0.02240040 0.06464319 predict(fit, new_data = pbc_orsf_test[1:5, ], pred_type = 'mort') ## [,1] ## [1,] 83.08611 ## [2,] 27.48146 ## [3,] 43.52432 ## [4,] 15.20281 ## [5,] 10.56334"},{"path":"https://bcjaeger.github.io/aorsf/reference/print.orsf_fit.html","id":null,"dir":"Reference","previous_headings":"","what":"Inspect your ORSF model — print.orsf_fit","title":"Inspect your ORSF model — print.orsf_fit","text":"Printing ORSF model tells : Linear combinations: identified? N observations: Number rows training data N events: Number events training data N trees: Number trees forest N predictors total: Total number columns predictor matrix N predictors per node: Number variables used linear combinations Average leaves per tree: proxy depth trees Min observations leaf: See leaf_min_obs orsf Min events leaf: See leaf_min_events orsf OOB stat value: --bag error fitting trees OOB stat type: --bag error computed? Variable importance: variable importance computed?","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/print.orsf_fit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inspect your ORSF model — print.orsf_fit","text":"","code":"# S3 method for orsf_fit print(x, ...)"},{"path":"https://bcjaeger.github.io/aorsf/reference/print.orsf_fit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inspect your ORSF model — print.orsf_fit","text":"x (orsf_fit) oblique random survival forest (ORSF; see orsf). ... arguments passed methods (currently used).","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/print.orsf_fit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inspect your ORSF model — print.orsf_fit","text":"x, invisibly.","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/print.orsf_fit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Inspect your ORSF model — print.orsf_fit","text":"","code":"object <- orsf(pbc_orsf, Surv(time, status) ~ . - id, n_tree = 5) print(object) #> ---------- Oblique random survival forest #> #> Linear combinations: Accelerated #> N observations: 276 #> N events: 111 #> N trees: 5 #> N predictors total: 17 #> N predictors per node: 5 #> Average leaves per tree: 27 #> Min observations in leaf: 5 #> Min events in leaf: 1 #> OOB stat value: 0.76 #> OOB stat type: Harrell's C-statistic #> Variable importance: anova #> #> -----------------------------------------"},{"path":"https://bcjaeger.github.io/aorsf/reference/print.orsf_summary_uni.html","id":null,"dir":"Reference","previous_headings":"","what":"Print ORSF summary — print.orsf_summary_uni","title":"Print ORSF summary — print.orsf_summary_uni","text":"Print ORSF summary","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/print.orsf_summary_uni.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print ORSF summary — print.orsf_summary_uni","text":"","code":"# S3 method for orsf_summary_uni print(x, n_variables = NULL, ...)"},{"path":"https://bcjaeger.github.io/aorsf/reference/print.orsf_summary_uni.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print ORSF summary — print.orsf_summary_uni","text":"x object class 'orsf_summary' n_variables number variables print ... arguments passed methods (currently used).","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/print.orsf_summary_uni.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print ORSF summary — print.orsf_summary_uni","text":"invisibly, x","code":""},{"path":"https://bcjaeger.github.io/aorsf/reference/print.orsf_summary_uni.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print ORSF summary — print.orsf_summary_uni","text":"","code":"object <- orsf(pbc_orsf, Surv(time, status) ~ . - id) smry <- orsf_summarize_uni(object, n_variables = 3) print(smry) #> #> -- bili (VI Rank: 1) --------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 0.80 0.2339194 0.1221911 0.05320592 0.3542689 #> 1.4 0.2633691 0.1536543 0.07504906 0.4054625 #> 3.5 0.3805454 0.2979334 0.16496936 0.5694776 #> #> -- copper (VI Rank: 2) ------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> 43 0.2721538 0.1431253 0.05662816 0.4678858 #> 74 0.2959924 0.1676301 0.06786956 0.5000693 #> 129 0.3494525 0.2324844 0.11008935 0.5605183 #> #> -- sex (VI Rank: 3) ---------------------------- #> #> |---------------- risk ----------------| #> Value Mean Median 25th % 75th % #> m 0.3602617 0.2525631 0.11691691 0.5969917 #> f 0.3068573 0.1538510 0.05301943 0.5580260 #> #> Predicted risk at time t = 1788 for top 3 predictors"},{"path":"https://bcjaeger.github.io/aorsf/news/index.html","id":"aorsf-010-unreleased","dir":"Changelog","previous_headings":"","what":"aorsf 0.1.0 (unreleased)","title":"aorsf 0.1.0 (unreleased)","text":"Re-worked aorsf’s C++, code following design ranger, set classification regression trees. Allowed multi-threading performed orsf(), predict.orsf_fit(), functions orsf_vi() orsf_pd() family. Allowed sampling without replacement sampling specific fraction observations orsf() Included Harrell’s C-statistic option assessing goodness splits growing trees. Fixed issue uninformative error message occur pred_horizon > max(time) orsf_summarize_uni. Thanks @JyHao1 @DustinMLong finding !","code":""},{"path":"https://bcjaeger.github.io/aorsf/news/index.html","id":"aorsf-007","dir":"Changelog","previous_headings":"","what":"aorsf 0.0.7","title":"aorsf 0.0.7","text":"CRAN release: 2023-01-12 Additional changes internal testing avoid problems ATLAS","code":""},{"path":"https://bcjaeger.github.io/aorsf/news/index.html","id":"aorsf-006","dir":"Changelog","previous_headings":"","what":"aorsf 0.0.6","title":"aorsf 0.0.6","text":"CRAN release: 2023-01-06 Minor fix internal tests failing run ATLAS","code":""},{"path":"https://bcjaeger.github.io/aorsf/news/index.html","id":"aorsf-005","dir":"Changelog","previous_headings":"","what":"aorsf 0.0.5","title":"aorsf 0.0.5","text":"CRAN release: 2022-12-14 orsf() longer throws errors warnings try give single predictor. note added documentation details ?orsf explains using single predictor orsf() somewhat useless. done resolve https://github.com/mlr-org/mlr3extralearners/issues/259. predict.orsf_fit now accepts pred_horizon = 0 returns sensible values. Thanks @mattwarkentin feature request. added function perform variable selection, orsf_vs(). Made variable importance consistent respect group_factors. Originally, output orsf ungrouped VI values orsf_vi grouped values. update, orsf defaults grouped values. ungrouped values can still recovered. Fixed issue orsf_pd functions output data returned original scale.","code":""},{"path":"https://bcjaeger.github.io/aorsf/news/index.html","id":"aorsf-004","dir":"Changelog","previous_headings":"","what":"aorsf 0.0.4","title":"aorsf 0.0.4","text":"CRAN release: 2022-11-07 orsf formulas now accepts Surv objects (see https://github.com/ropensci/aorsf/issues/11) Added verbose_progress input orsf, prints messages console indicating progress. Allowance missing values orsf. Mean mode imputation performed observations missing data. values can also used impute new data missing values. Centering scaling predictors now done prior growing forest.","code":""},{"path":"https://bcjaeger.github.io/aorsf/news/index.html","id":"aorsf-003","dir":"Changelog","previous_headings":"","what":"aorsf 0.0.3","title":"aorsf 0.0.3","text":"CRAN release: 2022-10-09 Included rOpenSci reviewers Christopher Jackson, Marvin N Wright, Lukas Burk DESCRIPTION reviewers. Thank ! Added clarification docs pros/cons different variable importance techniques Added regression tests aorsf versus obliqueRSF (similar) Additional support tests functions long right hand sides Updated --bag vignette appropriate custom functions. Allow status values input data general, .e., just 0 1. Allow missing values predict functions, including partial dependence.","code":""},{"path":"https://bcjaeger.github.io/aorsf/news/index.html","id":"aorsf-002","dir":"Changelog","previous_headings":"","what":"aorsf 0.0.2","title":"aorsf 0.0.2","text":"CRAN release: 2022-09-05 Modified unit tests compatibility extra checks run CRAN.","code":""},{"path":"https://bcjaeger.github.io/aorsf/news/index.html","id":"aorsf-001","dir":"Changelog","previous_headings":"","what":"aorsf 0.0.1","title":"aorsf 0.0.1","text":"CRAN release: 2022-08-23 Added orsf_control_custom(), allows users submit custom functions identifying linear combinations inputs growing oblique decision trees. Added weights input orsf, allowing users fit orsf specific data training set. Added chf mort options predict.orsf_fit(). Mortality predictions fully implemented yet - supported partial dependence --bag error estimates. features added future update.","code":""},{"path":"https://bcjaeger.github.io/aorsf/news/index.html","id":"aorsf-0009000","dir":"Changelog","previous_headings":"","what":"aorsf 0.0.0.9000","title":"aorsf 0.0.0.9000","text":"Core features implemented: fit, interpret, predict using oblique random survival forests. Vignettes + Readme covering usage core features. Website hosted GitHub pages, managed pkgdown.","code":""}]