From 1a52a06c7d951fb5adb3af0c75f29adaa3a7fc44 Mon Sep 17 00:00:00 2001 From: bcjaeger Date: Sat, 25 Nov 2023 18:50:29 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20ropensci?= =?UTF-8?q?/aorsf@11ed7a659e445566d6a45a16ec4cec85674369b8=20=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- articles/aorsf.html | 42 ++-- articles/fast.html | 6 +- articles/oobag.html | 4 +- .../figure-html/unnamed-chunk-2-1.png | Bin 28817 -> 28879 bytes articles/pd.html | 86 +++++---- .../figure-html/unnamed-chunk-10-1.png | Bin 0 -> 74417 bytes .../figure-html/unnamed-chunk-14-1.png | Bin 0 -> 101780 bytes .../figure-html/unnamed-chunk-9-1.png | Bin 74417 -> 48414 bytes index.html | 66 +++---- pkgdown.yml | 2 +- reference/as.data.table.orsf_summary_uni.html | 16 +- reference/index.html | 5 + reference/orsf.html | 181 +++++++++--------- reference/orsf_control_custom.html | 39 ++-- reference/orsf_ice_oob.html | 31 ++- reference/orsf_pd_oob.html | 45 ++--- reference/orsf_summarize_uni.html | 37 ++-- reference/orsf_time_to_train.html | 6 +- reference/orsf_update.html | 40 +++- reference/orsf_vs.html | 12 +- reference/pred_spec_auto.html | 143 ++++++++++++++ reference/print.ObliqueForest.html | 4 +- reference/print.orsf_summary_uni.html | 18 +- search.json | 2 +- sitemap.xml | 3 + 25 files changed, 485 insertions(+), 303 deletions(-) create mode 100644 articles/pd_files/figure-html/unnamed-chunk-10-1.png create mode 100644 articles/pd_files/figure-html/unnamed-chunk-14-1.png create mode 100644 reference/pred_spec_auto.html diff --git a/articles/aorsf.html b/articles/aorsf.html index fc5109ad..eac51007 100644 --- a/articles/aorsf.html +++ b/articles/aorsf.html @@ -134,10 +134,10 @@

Oblique RFs for #> N trees: 5 #> N predictors total: 17 #> N predictors per node: 5 -#> Average leaves per tree: 21.8 +#> Average leaves per tree: 22.4 #> Min observations in leaf: 5 #> Min events in leaf: 1 -#> OOB stat value: 0.79 +#> OOB stat value: 0.76 #> OOB stat type: Harrell's C-index #> Variable importance: anova #> @@ -160,9 +160,9 @@

Oblique RFs for #> N trees: 5 #> N predictors total: 7 #> N predictors per node: 3 -#> Average leaves per tree: 4.6 +#> Average leaves per tree: 6.2 #> Min observations in leaf: 5 -#> OOB stat value: 0.99 +#> OOB stat value: 0.98 #> OOB stat type: AUC-ROC #> Variable importance: anova #> @@ -184,9 +184,9 @@

Oblique RFs for #> N trees: 5 #> N predictors total: 7 #> N predictors per node: 3 -#> Average leaves per tree: 50.2 +#> Average leaves per tree: 51.8 #> Min observations in leaf: 5 -#> OOB stat value: 0.76 +#> OOB stat value: 0.72 #> OOB stat type: RSQ #> Variable importance: anova #> @@ -224,14 +224,12 @@

Variable importance
 
 orsf_vi_negate(pbc_fit)
-#>          bili       protime        copper        hepato         stage 
-#>  1.008476e-01  6.038004e-02  5.280360e-02  4.442335e-02  4.147581e-02 
-#>       ascites       spiders           age          chol       albumin 
-#>  3.755163e-02  2.950994e-02  2.563519e-02  1.857127e-02  1.735975e-02 
-#>           sex      platelet           trt          trig         edema 
-#>  1.564084e-02  1.476163e-02  1.286685e-02  2.331721e-03  4.482712e-05 
-#>      alk.phos           ast 
-#> -8.314402e-04 -8.102168e-03
+#> bili sex copper stage albumin ascites +#> 0.143819854 0.122445342 0.087808486 0.039759180 0.039732477 0.021734850 +#> spiders chol ast trt protime edema +#> 0.017444941 0.015308401 0.010300780 0.010046279 0.007966224 0.007160075 +#> alk.phos age platelet hepato trig +#> 0.006828621 -0.003289060 -0.005256892 -0.007843452 -0.010161909
  • You can also compute variable importance using @@ -241,10 +239,10 @@

    Variable importance
     
     orsf_vi_permute(penguin_fit)
    -#>    bill_length_mm            island     bill_depth_mm       body_mass_g 
    -#>       0.188477500       0.096518097       0.091798497       0.074326102 
    -#> flipper_length_mm               sex              year 
    -#>       0.032271836       0.010234658       0.006088009
    +#> flipper_length_mm island bill_length_mm bill_depth_mm +#> 0.1433690678 0.1147793265 0.1034367635 0.0693059825 +#> body_mass_g sex year +#> 0.0649575203 0.0123891035 -0.0003928778

  • A faster alternative to permutation and negation importance is @@ -253,10 +251,10 @@

    Variable importance
     
     orsf_vi_anova(bill_fit)
    -#>           species               sex flipper_length_mm     bill_depth_mm 
    -#>       0.416017316       0.234042553       0.082706767       0.080645161 
    -#>            island       body_mass_g              year 
    -#>       0.074466081       0.060000000       0.007575758
    +#> species sex island body_mass_g +#> 0.21747649 0.16363636 0.11045033 0.08695652 +#> flipper_length_mm bill_depth_mm year +#> 0.08088235 0.07964602 0.01801802

  • diff --git a/articles/fast.html b/articles/fast.html index b3c78e94..543f41f8 100644 --- a/articles/fast.html +++ b/articles/fast.html @@ -127,7 +127,7 @@

    Don’t specify a control# control_fast() is much faster time_net['elapsed'] / time_fast['elapsed'] #> elapsed -#> 46.18182 +#> 45.34783 +#> [1,] 0.7197145

    The out-of-bag estimate of 1 (the default method to evaluate -out-of-bag predictions) is 0.8043565.

    +out-of-bag predictions) is 0.7197145.

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    Three ways to compute PD
  • out-of-bag PD indicates relationships that the model has learned during training but using the out-of-bag data simulates application of -the model to new data. if you want to test your model’s reliability or -fairness in new data but you don’t have access to a large testing -set.

  • +the model to new data. This is helpful if you want to test your model’s +reliability or fairness in new data but you don’t have access to a large +testing set.

  • new data PD shows how the model predicts outcomes for observations it has not seen. This is helpful if you want to test your model’s reliability or fairness.

  • +

    +
    +

    Automatic variable values +

    +

    Use pred_spec_auto() if you know the variables you want +to check out but you don’t have a specific set of values in mind:

    +
    +
    +orsf_pd_inb(fit, pred_spec_auto(bili))
    +#>    pred_horizon  bili      mean        lwr       medn       upr
    +#> 1:      1826.25 0.600 0.1916845 0.01393675 0.09277438 0.7862712
    +#> 2:      1826.25 0.700 0.1931983 0.01407259 0.09367646 0.7862712
    +#> 3:      1826.25 1.300 0.2111518 0.01875820 0.10058691 0.8056634
    +#> 4:      1826.25 3.175 0.2707361 0.03183542 0.18335844 0.8402006
    +#> 5:      1826.25 7.110 0.4345519 0.22022836 0.36615264 0.8610052
    +

    pred_spec_auto() lets you specify a variable in your +model with or without quotes, and then assign values for that variable +on your behalf. For continuous variables, it uses quantiles (10, 25, 50, +75, and 90). For nominal variables, it uses unique categories.

    Let’s re-fit our ORSF model to all available data before proceeding to the next sections.

    -
    +
     
     set.seed(329730)
     
    @@ -218,9 +237,9 @@ 

    Three ways to compute PDOne variable, one horizon

    Computing PD for a single variable is straightforward:

    -
    +
     
    -pd_sex <- orsf_pd_oob(fit, pred_spec = list(sex = c("m", "f")))
    +pd_sex <- orsf_pd_oob(fit, pred_spec = pred_spec_auto(sex))
     
     pd_sex
     #>    pred_horizon sex      mean        lwr      medn       upr
    @@ -234,20 +253,20 @@ 

    One variable, moving horizon
    +
     
    -pd_sex_tv <- orsf_pd_oob(fit, pred_spec = list(sex = c("m", "f")),
    +pd_sex_tv <- orsf_pd_oob(fit, pred_spec = pred_spec_auto(sex),
                              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')
    -

    +

    From inspection, we can see that males have higher risk than females and the difference in that risk grows over time. This can also be seen by viewing the ratio of expected risk over time:

    -

    Multiple variables, marginally @@ -269,26 +288,27 @@

    Multiple variables, marginally
    +
     
     pd_two_vars <-  
      orsf_pd_oob(fit,
    -             pred_spec = list(sex = c("m", "f"), bili = 1:5),
    +             pred_spec = pred_spec_auto(sex, bili),
                  expand_grid = FALSE)
     
     pd_two_vars
     #>    pred_horizon variable value level      mean        lwr      medn       upr
     #> 1:      1826.25      sex    NA     m 0.3420353 0.01698975 0.2493781 0.9219013
     #> 2:      1826.25      sex    NA     f 0.3078882 0.01046657 0.1877641 0.9212616
    -#> 3:      1826.25     bili     1  <NA> 0.2532276 0.01046657 0.1725400 0.8360690
    -#> 4:      1826.25     bili     2  <NA> 0.2922156 0.01903910 0.2108751 0.8777507
    -#> 5:      1826.25     bili     3  <NA> 0.3429252 0.05466756 0.2786783 0.8797394
    -#> 6:      1826.25     bili     4  <NA> 0.4130722 0.10032446 0.3637241 0.8888114
    -#> 7:      1826.25     bili     5  <NA> 0.4417891 0.11537722 0.4001863 0.9132712
    -

    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.

    -
    +#> 3:      1826.25     bili 0.600  <NA> 0.2455223 0.01045620 0.1643707 0.8391940
    +#> 4:      1826.25     bili 0.800  <NA> 0.2505248 0.01046657 0.1666401 0.8360690
    +#> 5:      1826.25     bili 1.400  <NA> 0.2643678 0.01693072 0.1840421 0.8501067
    +#> 6:      1826.25     bili 3.525  <NA> 0.3841995 0.08224197 0.3260132 0.8888114
    +#> 7:      1826.25     bili 7.250  <NA> 0.5193563 0.18367045 0.5021500 0.9141342
    +

    To get a view of partial dependence for any number of variables in +the training data, use orsf_summarize_uni(). This function +computes out-of-bag PD for the most important n_variables +and returns a nicely formatted view of the output:

    +
     
     pd_smry <- orsf_summarize_uni(fit, n_variables = 4)
     
    @@ -326,9 +346,9 @@ 

    Multiple variables, marginally#> 129 0.3412883 0.2659864 0.11419673 0.5411165 #> #> Predicted risk at time t = 1826.25 for top 4 predictors

    -

    It’s easy enough to turn this ‘summary’ object into a -data.table for downstream plotting and tables.

    -
    +

    This ‘summary’ object can be converted into a data.table +for downstream plotting and tables.

    +
     
     head(as.data.table(pd_smry))
     #>    variable importance Value      Mean    Median     25th %    75th %
    @@ -353,11 +373,9 @@ 

    Multiple variables, jointlybili, edema, and trt:

    -
    +
     
    -pred_spec = list(bili = seq(1, 5, length.out = 20),
    -                 edema = levels(pbc_orsf_train$edema),
    -                 trt = levels(pbc_orsf$trt))
    +pred_spec = pred_spec_auto(bili, edema, trt)
     
     pd_bili_edema <- orsf_pd_oob(fit, pred_spec)
     
    @@ -366,7 +384,7 @@ 

    Multiple variables, jointlyggplot(pd_bili_edema, aes(x = bili, y = medn, col = trt, linetype = edema)) + geom_line() + labs(y = 'Expected predicted risk')

    -

    +

    From inspection,

    Now we can visualize the curves.

    -
    +
     
     library(ggplot2)
     
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z>>3R$bEI*1uk)AiF5F(L}Io|d;t(JoNWPjm&Fr3U7=9;8t;kx zG1_xF;W-u($J#FLh9;e4q>%;yi=Xs5R+o^Jyp$9Lq|y@`)^TwuQe|}B#g+{7ancXL zcjpBN50DRNN6iASPsF8Gh{FUZ$P)cWzQau=9?wEA`5rrCRWyKOX+<=ERLzH?D1}Ou zh^-L688DlFRu72>=vWxhK7~e4xR|4Tlc`!GUI?T!6BX~Py}<|+mKZ!fAArY+zBE5f zJ~*SU{{B=Jiv^p8=&7(Jd0)%dc`p4TiRY@Ga)$-`Bg`790{!rqMSMS|Q*-_TYyQ3@ qJ3AaPRpiS%90s>P;PU_Q=q|nXLijhcEHat=_vkCJQOZl{kA4Gfoy{@; diff --git a/index.html b/index.html index e019f844..dbbf9110 100644 --- a/index.html +++ b/index.html @@ -112,8 +112,7 @@

    Installation

    What is an oblique decision tree?

    -

    Decision trees are developed by splitting a set of training data into two new subsets, with the goal of having more similarity within the new subsets than between them. The splitting process is repeated on resulting subsets of data until a stopping criterion is met.

    -

    When the new subsets of data are formed based on a single predictor, the decision tree is said to be axis-based because the splits of the data appear perpendicular to the axis of the predictor. When linear combinations of variables are used instead of a single variable, the tree is oblique because the splits of the data are neither parallel nor at a right angle to the axis.

    +

    Decision trees are grown by splitting a set of training data into non-overlapping subsets, with the goal of having more similarity within the new subsets than between them. When subsets are created with a single predictor, the decision tree is axis-based because the subset boundaries are perpendicular to the axis of the predictor. When linear combinations (i.e., a weighted sum) of variables are used instead of a single variable, the tree is oblique because the boundaries are neither parallel nor perpendicular to the axis.

    Figure: Decision trees for classification with axis-based splitting (left) and oblique splitting (right). Cases are orange squares; controls are purple circles. Both trees partition the predictor space defined by variables X1 and X2, but the oblique splits do a better job of separating the two classes.

    @@ -150,10 +149,10 @@

    Inspect#> N trees: 500 #> N predictors total: 17 #> N predictors per node: 5 -#> Average leaves per tree: 10.198 +#> Average leaves per tree: 10.238 #> Min observations in leaf: 5 #> Min events in leaf: 1 -#> OOB stat value: 0.84 +#> OOB stat value: 0.83 #> OOB stat type: Harrell's C-index #> Variable importance: anova #> @@ -173,40 +172,36 @@

    Variable importance
     
     orsf_vi_negate(fit)
    -#>          bili           sex        copper         stage           age 
    -#>  0.1152040355  0.0550384871  0.0346540451  0.0342394602  0.0212811906 
    -#>           ast       protime        hepato          chol       albumin 
    -#>  0.0189147173  0.0179113542  0.0151705980  0.0111316204  0.0107352274 
    -#>         edema       ascites       spiders          trig      alk.phos 
    -#>  0.0101276579  0.0097629480  0.0043991365  0.0031611867  0.0030722039 
    -#>           trt      platelet 
    -#>  0.0024625381 -0.0005569787

    +#> bili sex copper stage age ast +#> 0.117180683 0.058528338 0.033761789 0.026655509 0.022144911 0.019139095 +#> protime hepato edema ascites albumin chol +#> 0.016879701 0.011605852 0.010634489 0.009580159 0.008336260 0.007633992 +#> trt spiders alk.phos trig platelet +#> 0.002705027 0.002662017 0.002413369 0.001197399 -0.003386483
  • permutation: Each variable is assessed separately by randomly permuting the variable’s values and then determining how much the model’s performance changes. The worse the model’s performance after permuting the values of a given variable, the more important the variable. This technique is flexible, intuitive, and frequently used. It also has several known limitations

     
     orsf_vi_permute(fit)
    -#>          bili         stage        copper           age           sex 
    -#>  0.0487808290  0.0180334035  0.0178325263  0.0124277288  0.0111897854 
    -#>           ast        hepato         edema       protime       ascites 
    -#>  0.0111084077  0.0095618267  0.0082559886  0.0082473977  0.0078725536 
    -#>       albumin          chol       spiders      alk.phos          trig 
    -#>  0.0073437819  0.0057881417  0.0033941136  0.0028194077  0.0019933887 
    -#>      platelet           trt 
    -#> -0.0002101522 -0.0016860907
    +#> bili copper age stage sex ast +#> 0.050536719 0.016394807 0.013793348 0.013204760 0.010261860 0.010101841 +#> edema ascites protime albumin hepato chol +#> 0.008298456 0.008148291 0.007630773 0.006667768 0.006141770 0.002881687 +#> spiders trig alk.phos platelet trt +#> 0.001669604 0.001047642 -0.000301684 -0.001417230 -0.001665785
  • analysis of variance (ANOVA)3: A p-value is computed for each coefficient in each linear combination of variables in each decision tree. Importance for an individual predictor variable is the proportion of times a p-value for its coefficient is < 0.01. This technique is very efficient computationally, but may not be as effective as permutation or negation in terms of selecting signal over noise variables. See Menze, 2011 for more details on this technique.

     
     orsf_vi_anova(fit)
    -#>       bili    ascites      edema        sex     copper      stage        age 
    -#> 0.48004315 0.43536122 0.38654727 0.31024531 0.29493088 0.27168950 0.26673985 
    -#>     hepato        ast    albumin    protime       chol    spiders       trig 
    -#> 0.21492921 0.20151679 0.18771331 0.18352060 0.14617169 0.13932292 0.13620489 
    -#>   alk.phos   platelet        trt 
    -#> 0.09051254 0.07011494 0.06179067
    +#> bili copper age stage sex ast +#> 0.050536719 0.016394807 0.013793348 0.013204760 0.010261860 0.010101841 +#> edema ascites protime albumin hepato chol +#> 0.008298456 0.008148291 0.007630773 0.006667768 0.006141770 0.002881687 +#> spiders trig alk.phos platelet trt +#> 0.001669604 0.001047642 -0.000301684 -0.001417230 -0.001665785
  • You can supply your own R function to estimate out-of-bag error when using negation or permutation importance (see oob vignette)

    @@ -224,16 +219,17 @@

    Partial dependence (PD)#> #> |---------------- Risk ----------------| #> Value Mean Median 25th % 75th % -#> 0.70 0.2099215 0.1262483 0.05057666 0.3160044 -#> 1.30 0.2244830 0.1472311 0.06196578 0.3369922 -#> 3.18 0.2932736 0.2263620 0.11896921 0.4424589 +#> 0.70 0.2043124 0.1288782 0.05502854 0.3130744 +#> 1.30 0.2193531 0.1430383 0.06680735 0.3352729 +#> 3.18 0.2835984 0.2210419 0.12363028 0.4313679 #> -#> -- ascites (VI Rank: 2) ------------------------ +#> -- copper (VI Rank: 2) ------------------------- #> #> |---------------- Risk ----------------| -#> Value Mean Median 25th % 75th % -#> 0 0.2630294 0.1490837 0.0613327 0.4186542 -#> 1 0.3924844 0.3053928 0.2222267 0.5253717 +#> Value Mean Median 25th % 75th % +#> 39.0 0.2308500 0.1358346 0.05536305 0.3575617 +#> 68.0 0.2415171 0.1482876 0.06189812 0.3682164 +#> 111 0.2725110 0.1846062 0.08723814 0.4047750 #> #> Predicted risk at time t = 1826.25 for top 2 predictors

    For more on PD, see the vignette

    @@ -253,7 +249,7 @@

    Comparison to existing software

    describes aorsf in detail with a summary of the procedures used in the tree fitting algorithm

  • runs a general benchmark comparing aorsf with obliqueRSF and several other learners

  • reports prediction accuracy and computational efficiency of all learners.

  • -
  • runs a simulation study comparing variable importance techniques with ORSFs, axis based RSFs, and boosted trees.

  • +
  • runs a simulation study comparing variable importance techniques with oblique survival RFs, axis based survival RFs, and boosted trees.

  • reports the probability that each variable importance technique will rank a relevant variable with higher importance than an irrelevant variable.

  • A more hands-on comparison of aorsf and other R packages is provided in orsf examples

    @@ -270,7 +266,7 @@

    References

    Funding

    -

    The developers of aorsf receive financial support from the Center for Biomedical Informatics, Wake Forest University School of Medicine. We also receive support from the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR001420.

    +

    The developers of aorsf received financial support from the Center for Biomedical Informatics, Wake Forest University School of Medicine. We also received support from the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR001420.

    The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

    diff --git a/pkgdown.yml b/pkgdown.yml index ae266c35..7af46996 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -6,7 +6,7 @@ articles: fast: fast.html oobag: oobag.html pd: pd.html -last_built: 2023-11-24T20:17Z +last_built: 2023-11-25T18:49Z 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 60b777c7..089b2616 100644 --- a/reference/as.data.table.orsf_summary_uni.html +++ b/reference/as.data.table.orsf_summary_uni.html @@ -96,14 +96,14 @@

    Examples as.data.table(smry) #> variable importance value mean medn lwr upr -#> 1: ascites 0.5187377 0 0.3063446 0.1990513 0.06515375 0.5230252 -#> 2: ascites 0.5187377 1 0.4884330 0.4365701 0.30549078 0.6587859 -#> 3: bili 0.4150588 0.80 0.2362416 0.1504088 0.05594773 0.3750936 -#> 4: bili 0.4150588 1.40 0.2576178 0.1747677 0.07608225 0.4075136 -#> 5: bili 0.4150588 3.52 0.3690934 0.3022574 0.17483479 0.5374230 -#> 6: edema 0.3067279 0 0.3024266 0.1940121 0.06177755 0.5241272 -#> 7: edema 0.3067279 0.5 0.3520858 0.2506729 0.10055139 0.5895461 -#> 8: edema 0.3067279 1 0.4535726 0.3788607 0.25229534 0.6466679 +#> 1: ascites 0.5033948 0 0.3095183 0.2045609 0.06652121 0.5262396 +#> 2: ascites 0.5033948 1 0.4737078 0.4199779 0.28338602 0.6549413 +#> 3: bili 0.4262452 0.80 0.2412249 0.1544592 0.06001328 0.3936507 +#> 4: bili 0.4262452 1.40 0.2619288 0.1743219 0.07577672 0.4229498 +#> 5: bili 0.4262452 3.52 0.3674699 0.3057508 0.17231452 0.5314688 +#> 6: edema 0.3082839 0 0.3049592 0.2004990 0.06096860 0.5281684 +#> 7: edema 0.3082839 0.5 0.3549742 0.2608154 0.11591837 0.5952879 +#> 8: edema 0.3082839 1 0.4535174 0.3852081 0.26480810 0.6297030 #> pred_horizon level #> 1: 1788 0 #> 2: 1788 1 diff --git a/reference/index.html b/reference/index.html index 22d381de..bc816086 100644 --- a/reference/index.html +++ b/reference/index.html @@ -173,6 +173,11 @@

    Partial depe orsf_pd_oob() orsf_pd_inb() orsf_pd_new()
    ORSF partial dependence
    +
    + + pred_spec_auto() +
    +
    Automatic variable values for dependence

    Example survival data

    diff --git a/reference/orsf.html b/reference/orsf.html index fc41d5ed..b456ce6a 100644 --- a/reference/orsf.html +++ b/reference/orsf.html @@ -486,7 +486,7 @@

    Examples## N trees: 500 ## N predictors total: 17 ## N predictors per node: 5 -## Average leaves per tree: 20.884 +## Average leaves per tree: 20.98 ## Min observations in leaf: 5 ## Min events in leaf: 1 ## OOB stat value: 0.84 @@ -638,25 +638,23 @@

    Linear combinations with you times = fit_accel$pred_horizon)

    The AUC values, from highest to lowest:

    sc$AUC$score[order(-AUC)]

    -

    ##     model times       AUC         se     lower     upper
    -##    <fctr> <num>     <num>      <num>     <num>     <num>
    -## 1:    net  1788 0.9151649 0.02027237 0.8754318 0.9548980
    -## 2:    rlt  1788 0.9119200 0.02092197 0.8709137 0.9529263
    -## 3:  accel  1788 0.9095628 0.02145474 0.8675122 0.9516133
    -## 4:    cph  1788 0.9095628 0.02145474 0.8675122 0.9516133
    -## 5:  rando  1788 0.9062197 0.02150724 0.8640663 0.9483731
    -## 6:    pca  1788 0.8983266 0.02305627 0.8531372 0.9435161

    +

    ##    model times       AUC         se     lower     upper
    +## 1:   net  1788 0.9134593 0.02079935 0.8726933 0.9542253
    +## 2:   rlt  1788 0.9129537 0.01979016 0.8741657 0.9517417
    +## 3: accel  1788 0.9112315 0.02098077 0.8701099 0.9523530
    +## 4:   cph  1788 0.9063871 0.02165434 0.8639453 0.9488288
    +## 5: rando  1788 0.9023489 0.02218936 0.8588586 0.9458393
    +## 6:   pca  1788 0.8994220 0.02201713 0.8562692 0.9425748

    And the indices of prediction accuracy:

    sc$Brier$score[order(-IPA), .(model, times, IPA)]

    ##         model times       IPA
    -##        <fctr> <num>     <num>
    -## 1:        net  1788 0.4905777
    -## 2:      accel  1788 0.4806649
    -## 3:        cph  1788 0.4806649
    -## 4:        rlt  1788 0.4675228
    -## 5:        pca  1788 0.4369636
    -## 6:      rando  1788 0.4302814
    -## 7: Null model  1788 0.0000000

    +## 1: net 1788 0.4916038 +## 2: accel 1788 0.4879683 +## 3: cph 1788 0.4751883 +## 4: rlt 1788 0.4640282 +## 5: pca 1788 0.4370592 +## 6: rando 1788 0.4258344 +## 7: Null model 1788 0.0000000

    From inspection,