diff --git a/man/orsf.Rd b/man/orsf.Rd
index 2c8931e7..4b818e8a 100644
--- a/man/orsf.Rd
+++ b/man/orsf.Rd
@@ -388,9 +388,9 @@ penguin_fit
## N trees: 5
## N predictors total: 7
## N predictors per node: 3
-## Average leaves per tree: 5.8
+## Average leaves per tree: 6
## Min observations in leaf: 5
-## OOB stat value: 0.99
+## OOB stat value: 0.98
## OOB stat type: AUC-ROC
## Variable importance: anova
##
@@ -415,9 +415,9 @@ bill_fit
## N trees: 5
## N predictors total: 7
## N predictors per node: 3
-## Average leaves per tree: 52
+## Average leaves per tree: 48.6
## Min observations in leaf: 5
-## OOB stat value: 0.74
+## OOB stat value: 0.73
## OOB stat type: RSQ
## Variable importance: anova
##
@@ -497,7 +497,7 @@ take to fit the forest before you commit to it:
orsf_time_to_train()
}\if{html}{\out{}}
-\if{html}{\out{
}}\preformatted{## Time difference of 1.744921 secs
+\if{html}{\out{
}}\preformatted{## Time difference of 2.529039 secs
}\if{html}{\out{
}}
\enumerate{
\item If fitting multiple forests, use the blueprint along with
@@ -568,12 +568,12 @@ brier_scores
\if{html}{\out{
}}\preformatted{## # A tibble: 6 x 4
## .metric .estimator .eval_time .estimate
##
-## 1 brier_survival standard 500 0.0268
-## 2 brier_survival standard 1000 0.0755
-## 3 brier_survival standard 1500 0.0663
-## 4 brier_survival standard 2000 0.0802
-## 5 brier_survival standard 2500 0.139
-## 6 brier_survival standard 3000 0.190
+## 1 brier_survival standard 500 0.0396
+## 2 brier_survival standard 1000 0.0685
+## 3 brier_survival standard 1500 0.0893
+## 4 brier_survival standard 2000 0.105
+## 5 brier_survival standard 2500 0.117
+## 6 brier_survival standard 3000 0.132
}\if{html}{\out{
}}
\if{html}{\out{
}}\preformatted{roc_scores <- test_pred \%>\%
@@ -585,12 +585,12 @@ roc_scores
\if{html}{\out{
}}\preformatted{## # A tibble: 6 x 4
## .metric .estimator .eval_time .estimate
##
-## 1 roc_auc_survival standard 500 0.977
-## 2 roc_auc_survival standard 1000 0.918
-## 3 roc_auc_survival standard 1500 0.964
+## 1 roc_auc_survival standard 500 0.966
+## 2 roc_auc_survival standard 1000 0.950
+## 3 roc_auc_survival standard 1500 0.942
## 4 roc_auc_survival standard 2000 0.944
-## 5 roc_auc_survival standard 2500 0.909
-## 6 roc_auc_survival standard 3000 0.891
+## 5 roc_auc_survival standard 2500 0.947
+## 6 roc_auc_survival standard 3000 0.953
}\if{html}{\out{
}}
}
}
diff --git a/man/orsf_control.Rd b/man/orsf_control.Rd
index 3009240f..ad2aff3c 100644
--- a/man/orsf_control.Rd
+++ b/man/orsf_control.Rd
@@ -310,13 +310,14 @@ The AUC values, from highest to lowest:
\if{html}{\out{
}}\preformatted{sc$AUC$score[order(-AUC)]
}\if{html}{\out{
}}
-\if{html}{\out{
}}\preformatted{## model times AUC se lower upper
-## 1: net 1788 0.9151649 0.02025057 0.8754745 0.9548553
-## 2: rlt 1788 0.9136390 0.02013475 0.8741756 0.9531024
-## 3: accel 1788 0.9095628 0.02143250 0.8675558 0.9515697
-## 4: cph 1788 0.9095628 0.02143250 0.8675558 0.9515697
-## 5: rando 1788 0.9062197 0.02148854 0.8641029 0.9483365
-## 6: pca 1788 0.8999479 0.02226683 0.8563057 0.9435901
+\if{html}{\out{
}}\preformatted{## model times AUC se lower upper
+##
+## 1: net 1788 0.9151649 0.02025057 0.8754745 0.9548553
+## 2: rlt 1788 0.9119200 0.02090107 0.8709547 0.9528854
+## 3: accel 1788 0.9095628 0.02143250 0.8675558 0.9515697
+## 4: cph 1788 0.9095628 0.02143250 0.8675558 0.9515697
+## 5: rando 1788 0.9062197 0.02148854 0.8641029 0.9483365
+## 6: pca 1788 0.8983266 0.02303267 0.8531834 0.9434698
}\if{html}{\out{
}}
And the indices of prediction accuracy:
@@ -325,11 +326,12 @@ And the indices of prediction accuracy:
}\if{html}{\out{
}}
\if{html}{\out{
}}\preformatted{## model times IPA
+##
## 1: net 1788 0.4905777
-## 2: accel 1788 0.4806065
-## 3: cph 1788 0.4806065
-## 4: rlt 1788 0.4687322
-## 5: pca 1788 0.4383995
+## 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
}\if{html}{\out{
}}
diff --git a/man/orsf_ice_oob.Rd b/man/orsf_ice_oob.Rd
index ed3208d4..96e74f3b 100644
--- a/man/orsf_ice_oob.Rd
+++ b/man/orsf_ice_oob.Rd
@@ -175,18 +175,20 @@ ice_oob <- orsf_ice_oob(fit_clsf, pred_spec = pred_spec)
ice_oob
}\if{html}{\out{
}}
-\if{html}{\out{
}}\preformatted{## id_variable id_row class flipper_length_mm pred
-## 1: 1 1 Adelie 190 0.92059968
-## 2: 1 2 Adelie 190 0.80953569
-## 3: 1 3 Adelie 190 0.84869374
-## 4: 1 4 Adelie 190 0.93559660
+\if{html}{\out{
}}\preformatted{## Key:
+## id_variable id_row class flipper_length_mm pred
+##
+## 1: 1 1 Adelie 190 0.92169247
+## 2: 1 2 Adelie 190 0.80944657
+## 3: 1 3 Adelie 190 0.85172955
+## 4: 1 4 Adelie 190 0.93559327
## 5: 1 5 Adelie 190 0.97708693
## ---
-## 896: 2 146 Gentoo 210 0.25636964
+## 896: 2 146 Gentoo 210 0.26092984
## 897: 2 147 Gentoo 210 0.04798334
-## 898: 2 148 Gentoo 210 0.07945140
-## 899: 2 149 Gentoo 210 0.84811899
-## 900: 2 150 Gentoo 210 0.10695367
+## 898: 2 148 Gentoo 210 0.07927359
+## 899: 2 149 Gentoo 210 0.84779971
+## 900: 2 150 Gentoo 210 0.11105143
}\if{html}{\out{
}}
There are two identifiers in the output:
@@ -240,6 +242,7 @@ ice_new
}\if{html}{\out{
}}
\if{html}{\out{
}}\preformatted{## id_variable id_row flipper_length_mm pred
+##
## 1: 1 1 190 37.94483
## 2: 1 2 190 37.61595
## 3: 1 3 190 37.53681
@@ -265,6 +268,7 @@ ice_new
}\if{html}{\out{
}}
\if{html}{\out{
}}\preformatted{## id_variable id_row species island body_mass_g pred
+##
## 1: 1 1 Adelie Biscoe 3200 37.78339
## 2: 1 2 Adelie Biscoe 3200 37.73273
## 3: 1 3 Adelie Biscoe 3200 37.71248
@@ -290,6 +294,7 @@ ice_new
}\if{html}{\out{
}}
\if{html}{\out{
}}\preformatted{## id_variable id_row variable value level pred
+##
## 1: 1 1 species NA Adelie 37.74136
## 2: 1 2 species NA Adelie 37.42367
## 3: 1 3 species NA Adelie 37.04598
@@ -318,6 +323,7 @@ ice_new
}\if{html}{\out{
}}
\if{html}{\out{
}}\preformatted{## id_variable id_row species island pred
+##
## 1: 1 1 Adelie Biscoe 38.52327
## 2: 1 2 Adelie Biscoe 38.32073
## 3: 1 3 Adelie Biscoe 37.71248
@@ -355,18 +361,19 @@ Compute individual conditional expectation using in-bag data for
ice_train
}\if{html}{\out{
}}
-\if{html}{\out{
}}\preformatted{## id_variable id_row pred_horizon bili pred
-## 1: 1 1 1826.25 1 0.1290317
-## 2: 1 2 1826.25 1 0.1242352
-## 3: 1 3 1826.25 1 0.0963452
-## 4: 1 4 1826.25 1 0.1172367
-## 5: 1 5 1826.25 1 0.2030256
-## ---
-## 746: 5 146 1826.25 5 0.7868537
-## 747: 5 147 1826.25 5 0.2012954
-## 748: 5 148 1826.25 5 0.4893605
-## 749: 5 149 1826.25 5 0.4698220
-## 750: 5 150 1826.25 5 0.9557285
+\if{html}{\out{
}}\preformatted{## id_variable id_row pred_horizon bili pred
+##
+## 1: 1 1 1826.25 1 0.1290317
+## 2: 1 2 1826.25 1 0.1242352
+## 3: 1 3 1826.25 1 0.0963452
+## 4: 1 4 1826.25 1 0.1172367
+## 5: 1 5 1826.25 1 0.2030256
+## ---
+## 746: 5 146 1826.25 5 0.7868537
+## 747: 5 147 1826.25 5 0.2012954
+## 748: 5 148 1826.25 5 0.4893605
+## 749: 5 149 1826.25 5 0.4698220
+## 750: 5 150 1826.25 5 0.9557285
}\if{html}{\out{
}}
If you don’t have specific values of a variable in mind, let
@@ -376,18 +383,19 @@ If you don’t have specific values of a variable in mind, let
ice_train
}\if{html}{\out{
}}
-\if{html}{\out{
}}\preformatted{## id_variable id_row pred_horizon bili pred
-## 1: 1 1 1826.25 0.59 0.11706741
-## 2: 1 2 1826.25 0.59 0.11562173
-## 3: 1 3 1826.25 0.59 0.09110739
-## 4: 1 4 1826.25 0.59 0.10069721
-## 5: 1 5 1826.25 0.59 0.18769751
-## ---
-## 746: 5 146 1826.25 7.21 0.82600898
-## 747: 5 147 1826.25 7.21 0.29156437
-## 748: 5 148 1826.25 7.21 0.58220919
-## 749: 5 149 1826.25 7.21 0.54168688
-## 750: 5 150 1826.25 7.21 0.96204106
+\if{html}{\out{
}}\preformatted{## id_variable id_row pred_horizon bili pred
+##
+## 1: 1 1 1826.25 0.55 0.11728559
+## 2: 1 2 1826.25 0.55 0.11728839
+## 3: 1 3 1826.25 0.55 0.08950739
+## 4: 1 4 1826.25 0.55 0.10064959
+## 5: 1 5 1826.25 0.55 0.18736417
+## ---
+## 746: 5 146 1826.25 7.25 0.82600898
+## 747: 5 147 1826.25 7.25 0.29156437
+## 748: 5 148 1826.25 7.25 0.58395919
+## 749: 5 149 1826.25 7.25 0.54202021
+## 750: 5 150 1826.25 7.25 0.96391985
}\if{html}{\out{
}}
Specify \code{pred_horizon} to get individual conditional expectation at each
@@ -398,18 +406,19 @@ value:
ice_train
}\if{html}{\out{
}}
-\if{html}{\out{
}}\preformatted{## id_variable id_row pred_horizon bili pred
-## 1: 1 1 500 0.59 0.008276627
-## 2: 1 1 1000 0.59 0.055715858
-## 3: 1 1 1500 0.59 0.084987224
-## 4: 1 1 2000 0.59 0.123090885
-## 5: 1 1 2500 0.59 0.165214938
-## ---
-## 4496: 5 150 1000 7.21 0.835895969
-## 4497: 5 150 1500 7.21 0.932657591
-## 4498: 5 150 2000 7.21 0.965944498
-## 4499: 5 150 2500 7.21 0.970325309
-## 4500: 5 150 3000 7.21 0.979051377
+\if{html}{\out{
}}\preformatted{## id_variable id_row pred_horizon bili pred
+##
+## 1: 1 1 500 0.55 0.008276627
+## 2: 1 1 1000 0.55 0.055724516
+## 3: 1 1 1500 0.55 0.085091120
+## 4: 1 1 2000 0.55 0.123423352
+## 5: 1 1 2500 0.55 0.166380739
+## ---
+## 4496: 5 150 1000 7.25 0.837774757
+## 4497: 5 150 1500 7.25 0.934536379
+## 4498: 5 150 2000 7.25 0.967823286
+## 4499: 5 150 2500 7.25 0.972059574
+## 4500: 5 150 3000 7.25 0.980785643
}\if{html}{\out{
}}
Multi-prediction horizon ice comes with minimal extra computational
diff --git a/man/orsf_pd_oob.Rd b/man/orsf_pd_oob.Rd
index 1412c818..c732e57e 100644
--- a/man/orsf_pd_oob.Rd
+++ b/man/orsf_pd_oob.Rd
@@ -195,13 +195,15 @@ pd_oob <- orsf_pd_oob(fit_clsf, pred_spec = pred_spec)
pd_oob
}\if{html}{\out{
}}
-\if{html}{\out{
}}\preformatted{## class flipper_length_mm mean lwr medn upr
-## 1: Adelie 190 0.6180632 0.207463688 0.76047056 0.9809703
-## 2: Adelie 210 0.4346177 0.018583256 0.56486883 0.8647387
-## 3: Chinstrap 190 0.2119948 0.017692341 0.15658268 0.7163635
-## 4: Chinstrap 210 0.1801186 0.020454479 0.09525310 0.7085293
-## 5: Gentoo 190 0.1699420 0.001277844 0.02831331 0.5738689
-## 6: Gentoo 210 0.3852637 0.068685035 0.20853993 0.9537020
+\if{html}{\out{
}}\preformatted{## Key:
+## class flipper_length_mm mean lwr medn upr
+##
+## 1: Adelie 190 0.6176908 0.202278109 0.75856417 0.9810614
+## 2: Adelie 210 0.4338528 0.019173811 0.56489202 0.8648110
+## 3: Chinstrap 190 0.2114979 0.017643385 0.15211271 0.7215181
+## 4: Chinstrap 210 0.1803019 0.020108201 0.09679464 0.7035053
+## 5: Gentoo 190 0.1708113 0.001334861 0.02769695 0.5750201
+## 6: Gentoo 210 0.3858453 0.068685035 0.20717073 0.9532853
}\if{html}{\out{
}}
Note that predicted probabilities are returned for each class and
@@ -220,7 +222,7 @@ But this isn’t the case for the median predicted probability!
\if{html}{\out{
}}\preformatted{sum(pd_oob[flipper_length_mm == 190, medn])
}\if{html}{\out{
}}
-\if{html}{\out{
}}\preformatted{## [1] 0.9453666
+\if{html}{\out{
}}\preformatted{## [1] 0.9383738
}\if{html}{\out{
}}
}
@@ -252,6 +254,7 @@ pd_new
}\if{html}{\out{
}}
\if{html}{\out{
}}\preformatted{## flipper_length_mm mean lwr medn upr
+##
## 1: 190 42.96571 37.09805 43.69769 48.72301
## 2: 210 45.66012 40.50693 46.31577 51.65163
}\if{html}{\out{
}}
@@ -268,6 +271,7 @@ pd_new
}\if{html}{\out{
}}
\if{html}{\out{
}}\preformatted{## species island body_mass_g mean lwr medn upr
+##
## 1: Adelie Biscoe 3200 40.31374 37.24373 40.31967 44.22824
## 2: Chinstrap Biscoe 3200 45.10582 42.63342 45.10859 47.60119
## 3: Gentoo Biscoe 3200 42.81649 40.19221 42.55664 46.84035
@@ -328,6 +332,7 @@ pd_new
}\if{html}{\out{
}}
\if{html}{\out{
}}\preformatted{## variable value level mean lwr medn upr
+##
## 1: species NA Adelie 41.90271 37.10417 41.51723 48.51478
## 2: species NA Chinstrap 47.11314 42.40419 46.96478 51.51392
## 3: species NA Gentoo 44.37038 39.87306 43.89889 51.21635
@@ -356,6 +361,7 @@ pd_new
}\if{html}{\out{
}}
\if{html}{\out{
}}\preformatted{## species island mean lwr medn upr
+##
## 1: Adelie Biscoe 41.98024 37.22711 41.65252 48.51478
}\if{html}{\out{
}}
}
@@ -382,12 +388,13 @@ Compute partial dependence using in-bag data for \code{bili = c(1,2,3,4,5)}:
pd_train
}\if{html}{\out{
}}
-\if{html}{\out{}}\preformatted{## pred_horizon bili mean lwr medn upr
-## 1: 1826.25 0.590 0.2484695 0.02035041 0.1243120 0.8823385
-## 2: 1826.25 0.725 0.2508045 0.02060111 0.1274237 0.8836536
-## 3: 1826.25 1.500 0.2797763 0.03964900 0.1601715 0.9041584
-## 4: 1826.25 3.500 0.3959349 0.13431288 0.2920400 0.9501230
-## 5: 1826.25 7.210 0.5344511 0.27869513 0.4651185 0.9782084
+##
+## 1: 1826.25 0.55 0.2481444 0.02035041 0.1242215 0.8801444
+## 2: 1826.25 0.70 0.2502831 0.02045039 0.1271039 0.8836536
+## 3: 1826.25 1.50 0.2797763 0.03964900 0.1601715 0.9041584
+## 4: 1826.25 3.50 0.3959349 0.13431288 0.2920400 0.9501230
+## 5: 1826.25 7.25 0.5351935 0.28064629 0.4652185 0.9783000
}\if{html}{\out{
}}
Specify \code{pred_horizon} to get partial dependence at each value:
@@ -412,38 +420,39 @@ Specify \code{pred_horizon} to get partial dependence at each value:
pd_train
}\if{html}{\out{}}
-\if{html}{\out{}}\preformatted{## Adelie Chinstrap Gentoo
-## [1,] 0.9405286 0.04125900 0.018212368
-## [2,] 0.9628964 0.03459853 0.002505059
-## [3,] 0.9029383 0.08527806 0.011783605
-## [4,] 0.9301983 0.05180907 0.017992625
-## [5,] 0.7968234 0.16538539 0.037791201
+## [1,] 0.9405310 0.04121955 0.018249405
+## [2,] 0.9628988 0.03455909 0.002542096
+## [3,] 0.9032074 0.08510528 0.011687309
+## [4,] 0.9300133 0.05209040 0.017896329
+## [5,] 0.7965703 0.16243492 0.040994821
}\if{html}{\out{
}}
\if{html}{\out{