From 0b03beb1779fd5d9478771894ed00098de109a7a Mon Sep 17 00:00:00 2001
From: bcjaeger You can also compute variable importance using permutation, a
@@ -187,13 +187,13 @@ A faster alternative to permutation and negation importance is
@@ -203,11 +203,11 @@ Because R is a single threaded language, multi-threading cannot be
applied when While these default values do make The out-of-bag estimate of Harrell’s C-statistic (the default method
-to evaluate out-of-bag predictions) is 0.8415542.Variable importance
orsf_vi_negate(orsf_fit)
#> bili copper sex age protime stage
-#> 0.121621470 0.047409632 0.036160756 0.023453468 0.023036042 0.021079271
+#> 0.121610913 0.047724351 0.035987586 0.023472835 0.023013176 0.020959499
#> albumin ascites ast chol edema trt
-#> 0.019171998 0.014530950 0.011655566 0.011520295 0.007794807 0.007008191
+#> 0.019390481 0.014457412 0.011659769 0.011544114 0.007728580 0.006902781
#> spiders alk.phos hepato trig platelet
-#> 0.004761188 0.003541539 0.003034140 0.002904610 0.001811919
+#> 0.004752062 0.003464822 0.003092596 0.002763978 0.001742350
Variable importance
orsf_vi_permute(orsf_fit)
#> bili copper stage age protime
-#> 0.0564823961 0.0223855853 0.0142026653 0.0135127275 0.0125608069
+#> 0.0563410637 0.0223514680 0.0140882841 0.0136856141 0.0125390739
#> ascites albumin ast edema chol
-#> 0.0115005317 0.0111892767 0.0062583603 0.0059328841 0.0049633185
+#> 0.0114579347 0.0112609663 0.0063723451 0.0058601940 0.0049876670
#> spiders sex alk.phos trig hepato
-#> 0.0047712233 0.0029539032 0.0021892028 0.0012938183 0.0009293975
+#> 0.0046062839 0.0029547526 0.0020493151 0.0013490654 0.0009248580
#> platelet trt
-#> 0.0003236044 -0.0010373711
+#> 0.0002087435 -0.0010805504
Variable importance
orsf_vi_anova(orsf_fit)
#> ascites bili edema copper age albumin protime
-#> 0.42817294 0.30059141 0.28426857 0.21943005 0.20141521 0.19357326 0.18584305
+#> 0.42926829 0.30005133 0.28307417 0.21974605 0.20161698 0.19367284 0.18475384
#> stage ast chol sex spiders hepato trig
-#> 0.16470910 0.15964407 0.15792194 0.15172667 0.14802844 0.13351750 0.12661567
+#> 0.16479913 0.15931052 0.15823438 0.15274199 0.14765751 0.13382443 0.12667896
#> alk.phos platelet trt
-#> 0.12163561 0.10985915 0.09887394
+#> 0.12193232 0.10931900 0.09903714
What about the original ORSF?)
)
#> user system elapsed
-#> 4.722 0.004 4.728
+#> 4.175 0.000 4.175
# and how long it takes to fit 50 cph trees
print(
@@ -271,11 +271,11 @@
What about the original ORSF?)
)
#> user system elapsed
-#> 0.056 0.000 0.056
+#> 0.051 0.000 0.051
t1['elapsed'] / t2['elapsed']
#> elapsed
-#> 84.42857
+#> 81.86275
aorsf and other machine learning software
diff --git a/articles/fast.html b/articles/fast.html
index 9e827451..f86e102c 100644
--- a/articles/fast.html
+++ b/articles/fast.html
@@ -150,7 +150,7 @@
Use
orsf_control_fast()
# control_fast() is much faster
time_net['elapsed'] / time_fast['elapsed']
#> elapsed
-#> 38.38776Use
n_thread
@@ -183,10 +183,10 @@ Use
n_thread
time_1_thread['elapsed'] / time_5_thread['elapsed']
#> elapsed
-#> 0.3702316
+#> 0.3305418
time_1_thread['elapsed'] / time_auto_thread['elapsed']
#> elapsed
-#> 0.7542064orsf()
needs to call R functions from C++,
which occurs when a customized R function is used to find linear
@@ -222,8 +222,8 @@ Do less
# about two times faster than auto thread with defaults
time_auto_thread['elapsed'] / time_lightweight['elapsed']
-#> elapsed
-#> 2.560515
+#> elapsed
+#> 2.53372
orsf()
run slower,
they also usually make its predictions more accurate or make the fit
easier to interpret.Show progress= 0,
n_tree = 500,
verbose_progress = TRUE)
-#> Growing trees: 18%. ~ time remaining: 8 seconds.
-#> Growing trees: 39%. ~ time remaining: 6 seconds.
-#> Growing trees: 56%. ~ time remaining: 4 seconds.
-#> Growing trees: 75%. ~ time remaining: 2 seconds.
-#> Growing trees: 96%. ~ time remaining: 0 seconds.
+#> Growing trees: 17%. ~ time remaining: 9 seconds.
+#> Growing trees: 33%. ~ time remaining: 8 seconds.
+#> Growing trees: 50%. ~ time remaining: 6 seconds.
+#> Growing trees: 68%. ~ time remaining: 3 seconds.
+#> Growing trees: 86%. ~ time remaining: 1 seconds.
#> Growing trees: 100%.
#> Computing predictions: 100%.
diff --git a/articles/oobag.html b/articles/oobag.html
index 17acd8a0..9ebb5d39 100644
--- a/articles/oobag.html
+++ b/articles/oobag.html
@@ -139,9 +139,9 @@
Out-of-bag predictions and error# what is the output from this function?
fit$eval_oobag$stat_values
#> [,1]
-#> [1,] 0.8415542
+#> [1,] 0.8403563
oobag_fun_brier(y_mat = pbc_orsf[,c('time', 'status')],
s_vec = fit$pred_oobag)
-#> [1] 0.113788
Second, you can pass your function into orsf()
, and it
will be used in place of Harrell’s C-statistic:
@@ -327,12 +327,12 @@User-supplied function importance = 'negate') fit_tdep_cstat$importance -#> bili copper sex stage albumin age -#> 0.109547520 0.048633340 0.031563870 0.024989400 0.023426140 0.020986680 -#> protime chol ascites ast edema trt -#> 0.019175230 0.018283250 0.017884150 0.015850050 0.008054587 0.007685040 -#> hepato spiders alk.phos platelet trig -#> 0.006622980 0.006348670 0.004479150 0.002303970 0.002085840
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