diff --git a/articles/aorsf.html b/articles/aorsf.html index 35e04107..d0f7c387 100644 --- a/articles/aorsf.html +++ b/articles/aorsf.html @@ -174,11 +174,11 @@

Variable importance orsf_vi_negate(orsf_fit) #> bili copper sex stage protime albumin -#> 0.1270712940 0.0468736844 0.0356362389 0.0238803902 0.0237785257 0.0227162723 +#> 0.1269333300 0.0471985360 0.0360365677 0.0243587309 0.0241708554 0.0225453236 #> age chol ascites ast spiders edema -#> 0.0212402311 0.0151047100 0.0136972183 0.0115757344 0.0075645891 0.0070613053 +#> 0.0213716886 0.0153575882 0.0139061009 0.0116817406 0.0075489002 0.0070203635 #> hepato trt alk.phos trig platelet -#> 0.0046737401 0.0039891475 0.0036234567 0.0033110318 0.0001873165 +#> 0.0049274049 0.0039949547 0.0034672829 0.0030764514 0.0004390764

  • You can also compute variable importance using permutation, a @@ -187,13 +187,13 @@

    Variable importance orsf_vi_permute(orsf_fit) #> bili copper protime albumin ascites -#> 0.0575909038 0.0227243825 0.0130008680 0.0128293701 0.0125212404 +#> 0.0572634486 0.0230199191 0.0133977370 0.0128399789 0.0126925475 #> age stage chol ast spiders -#> 0.0122147306 0.0112543262 0.0073212780 0.0063643033 0.0057172376 +#> 0.0125845408 0.0114992408 0.0074376943 0.0065136448 0.0059747612 #> edema hepato sex alk.phos trig -#> 0.0050999436 0.0025537138 0.0025179407 0.0012971186 0.0007953116 +#> 0.0050913001 0.0026642521 0.0024517816 0.0011030094 0.0006337896 #> trt platelet -#> -0.0004017700 -0.0008714547 +#> -0.0002731314 -0.0008234638

  • A faster alternative to permutation and negation importance is @@ -202,12 +202,12 @@

    Variable importance
     
 orsf_vi_anova(orsf_fit)
-#>    ascites       bili      edema     copper    albumin        age    protime 
-#> 0.49651047 0.42709313 0.30354480 0.30014500 0.25575816 0.25384974 0.24850000 
+#>    ascites       bili     copper      edema    albumin        age    protime 
+#> 0.49453823 0.42999054 0.30285438 0.30172086 0.25564632 0.25478301 0.24861878 
 #>      stage       chol        ast     hepato    spiders        sex       trig 
-#> 0.20914346 0.19153032 0.17189030 0.16325370 0.16284275 0.14321295 0.13550688 
+#> 0.20975354 0.19204981 0.17066015 0.16363636 0.16357504 0.14437500 0.13601630 
 #>   alk.phos   platelet        trt 
-#> 0.10722834 0.08011583 0.07639621
    +#> 0.10744986 0.08044316 0.07594937

    • @@ -259,7 +259,7 @@

    What about the original ORSF?) ) #> user system elapsed -#> 5.693 0.004 5.698 +#> 4.239 0.004 4.246 # 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 +#> 0.043 0.000 0.042 t1['elapsed'] / t2['elapsed'] -#> elapsed -#> 101.75 +#> elapsed +#> 101.0952

    aorsf and other machine learning software diff --git a/articles/fast.html b/articles/fast.html index b640df6d..c519ccb2 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 -#> 43.80135

    +#> 33.81481

    Use n_thread @@ -182,11 +182,11 @@

    Use n_thread# 5 threads and auto thread are both about 3 times faster than one thread time_1_thread['elapsed'] / time_5_thread['elapsed'] -#> elapsed -#> 0.527379 +#> elapsed +#> 0.3607971 time_1_thread['elapsed'] / time_auto_thread['elapsed'] #> elapsed -#> 0.8458431

    +#> 0.8261258

    Because R is a single threaded language, multi-threading cannot be applied when orsf() needs to call R functions from C++, which occurs when a customized R function is used to find linear @@ -223,7 +223,7 @@

    Do less# about two times faster than auto thread with defaults time_auto_thread['elapsed'] / time_lightweight['elapsed'] #> elapsed -#> 2.315189 +#> 2.106121

    While these default values do make orsf() run slower, they also usually make its predictions more accurate or make the fit easier to interpret.

    @@ -241,12 +241,11 @@

    Show progress= 0, n_tree = 500, verbose_progress = TRUE) -#> Growing trees: 15%. ~ time remaining: 10 seconds. -#> Growing trees: 32%. ~ time remaining: 8 seconds. -#> Growing trees: 50%. ~ time remaining: 6 seconds. -#> Growing trees: 67%. ~ time remaining: 3 seconds. -#> Growing trees: 82%. ~ time remaining: 2 seconds. -#> Growing trees: 96%. ~ time remaining: 0 seconds. +#> Growing trees: 20%. ~ time remaining: 8 seconds. +#> Growing trees: 41%. ~ time remaining: 5 seconds. +#> Growing trees: 58%. ~ time remaining: 4 seconds. +#> Growing trees: 78%. ~ time remaining: 2 seconds. +#> Growing trees: 97%. ~ time remaining: 0 seconds. #> Growing trees: 100%. #> Computing predictions: 100%. diff --git a/articles/oobag.html b/articles/oobag.html index c993d43c..9eafde4b 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,] 0.8411376 +#> [1,] 0.8382207

    The out-of-bag estimate of Harrell’s C-statistic (the default method -to evaluate out-of-bag predictions) is 0.8411376.

    +to evaluate out-of-bag predictions) is 0.8382207.

    Monitoring out-of-bag error @@ -204,7 +204,7 @@

    User-supplied out-of-bag oobag_fun_brier(y_mat = pbc_orsf[,c('time', 'status')], s_vec = fit$pred_oobag) -#> [1] 0.113399

    +#> [1] 0.103012

    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         age     protime       stage 
-#> 0.135791530 0.051558880 0.033970150 0.022342230 0.021744550 0.020032660 
-#>     albumin     ascites        chol         ast       edema     spiders 
-#> 0.017664360 0.014590300 0.012003390 0.010647330 0.008558551 0.008320780 
-#>        trig         trt      hepato    alk.phos    platelet 
-#> 0.006191100 0.004287580 0.004037580 0.003134560 0.000168460
    +#> bili copper sex protime age stage +#> 0.124912090 0.043697760 0.033879750 0.025204750 0.020195300 0.019374110 +#> albumin ast ascites spiders edema hepato +#> 0.018473550 0.012392770 0.011797150 0.007601810 0.006527248 0.006304720 +#> trig chol trt alk.phos platelet +#> 0.004538940 0.004452200 0.002477610 0.001515750 0.000205280

    Notes 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 26d3bae4..4d9d3c6d 100644 Binary files a/articles/oobag_files/figure-html/unnamed-chunk-2-1.png and b/articles/oobag_files/figure-html/unnamed-chunk-2-1.png differ diff --git a/articles/oobag_files/figure-html/unnamed-chunk-4-1.png b/articles/oobag_files/figure-html/unnamed-chunk-4-1.png index 9ff4c925..fcd5e1c5 100644 Binary files a/articles/oobag_files/figure-html/unnamed-chunk-4-1.png and b/articles/oobag_files/figure-html/unnamed-chunk-4-1.png differ diff --git a/articles/oobag_files/figure-html/unnamed-chunk-7-1.png b/articles/oobag_files/figure-html/unnamed-chunk-7-1.png index 0e0d1aa4..3ce9bd55 100644 Binary files a/articles/oobag_files/figure-html/unnamed-chunk-7-1.png and b/articles/oobag_files/figure-html/unnamed-chunk-7-1.png differ diff --git a/articles/oobag_files/figure-html/unnamed-chunk-8-1.png b/articles/oobag_files/figure-html/unnamed-chunk-8-1.png index 237c959e..866ee56b 100644 Binary files a/articles/oobag_files/figure-html/unnamed-chunk-8-1.png and b/articles/oobag_files/figure-html/unnamed-chunk-8-1.png differ diff --git a/articles/pd.html b/articles/pd.html index e664bf49..df2d5784 100644 --- a/articles/pd.html +++ b/articles/pd.html @@ -154,11 +154,11 @@

    Three ways to compute PD pd_inb #> pred_horizon bili mean lwr medn upr -#> 1: 1826.25 1 0.2044640 0.02107889 0.1030418 0.7753714 -#> 2: 1826.25 2 0.2371112 0.03377143 0.1311166 0.8015942 -#> 3: 1826.25 3 0.2784853 0.05468892 0.1832961 0.8171167 -#> 4: 1826.25 4 0.3285091 0.09241772 0.2436745 0.8343817 -#> 5: 1826.25 5 0.3640617 0.12596450 0.2808051 0.8375179

    +#> 1: 1826.25 1 0.2044391 0.02113505 0.1034119 0.7749208 +#> 2: 1826.25 2 0.2368157 0.03464873 0.1314807 0.8017311 +#> 3: 1826.25 3 0.2781822 0.05481241 0.1837971 0.8172536 +#> 4: 1826.25 4 0.3284357 0.09236425 0.2445730 0.8342811 +#> 5: 1826.25 5 0.3640157 0.12681296 0.2836405 0.8374174

  • using out-of-bag predictions for the training data

    @@ -168,11 +168,11 @@

    Three ways to compute PD pd_oob #> pred_horizon bili mean lwr medn upr -#> 1: 1826.25 1 0.2050548 0.02128050 0.1117025 0.7748756 -#> 2: 1826.25 2 0.2372927 0.03525888 0.1416681 0.7976469 -#> 3: 1826.25 3 0.2779018 0.05380047 0.1809783 0.8060741 -#> 4: 1826.25 4 0.3281057 0.09365158 0.2374257 0.8235845 -#> 5: 1826.25 5 0.3632041 0.12856484 0.2774458 0.8245021 +#> 1: 1826.25 1 0.2048918 0.02172816 0.1122952 0.7771277 +#> 2: 1826.25 2 0.2366722 0.03613510 0.1416972 0.7976469 +#> 3: 1826.25 3 0.2773803 0.05492895 0.1808253 0.8060741 +#> 4: 1826.25 4 0.3278401 0.09472187 0.2400870 0.8235845 +#> 5: 1826.25 5 0.3629808 0.12856484 0.2788031 0.8245021

  • using predictions for a new set of data

    @@ -184,11 +184,11 @@

    Three ways to compute PD pd_test #> pred_horizon bili mean lwr medn upr -#> 1: 1826.25 1 0.2439115 0.02172942 0.1779830 0.8041872 -#> 2: 1826.25 2 0.2756952 0.03962448 0.2154036 0.8222641 -#> 3: 1826.25 3 0.3183053 0.06352379 0.2685873 0.8375648 -#> 4: 1826.25 4 0.3688563 0.10419502 0.3253473 0.8540203 -#> 5: 1826.25 5 0.4044202 0.13842629 0.3598379 0.8570826 +#> 1: 1826.25 1 0.2438984 0.02179705 0.1779682 0.8047647 +#> 2: 1826.25 2 0.2754862 0.03964752 0.2152529 0.8222340 +#> 3: 1826.25 3 0.3180431 0.06354033 0.2671143 0.8375648 +#> 4: 1826.25 4 0.3688671 0.10422564 0.3255283 0.8540203 +#> 5: 1826.25 5 0.4044729 0.13846933 0.3595994 0.8567715

  • in-bag PD indicates relationships that the model has learned during training. This is helpful if your goal is to interpret the @@ -221,9 +221,9 @@

    One variable, one horizonpd_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 0.3450763 0.04262670 0.2341746 0.9328723 -#> 2: 1826.25 f 0.2969945 0.01318148 0.1582882 0.9444627 +#> pred_horizon sex mean lwr medn upr +#> 1: 1826.25 m 0.3447674 0.0427366 0.2348158 0.9327978 +#> 2: 1826.25 f 0.2970869 0.0136574 0.1559604 0.9436715

    The output shows that the expected predicted mortality risk for men is substantially higher than women at 5 years after baseline.

    @@ -276,13 +276,13 @@

    Multiple variables, marginallypd_two_vars #> pred_horizon variable value level mean lwr medn upr -#> 1: 1826.25 sex NA m 0.3450763 0.04262670 0.2341746 0.9328723 -#> 2: 1826.25 sex NA f 0.2969945 0.01318148 0.1582882 0.9444627 -#> 3: 1826.25 bili 1 <NA> 0.2377193 0.01619342 0.1306291 0.8564427 -#> 4: 1826.25 bili 2 <NA> 0.2859169 0.03904868 0.1794162 0.8929878 -#> 5: 1826.25 bili 3 <NA> 0.3361329 0.06484990 0.2390641 0.9087875 -#> 6: 1826.25 bili 4 <NA> 0.3826736 0.08863984 0.3029143 0.9107574 -#> 7: 1826.25 bili 5 <NA> 0.4165132 0.12050963 0.3450846 0.9146856 +#> 1: 1826.25 sex NA m 0.3447674 0.04273660 0.2348158 0.9327978 +#> 2: 1826.25 sex NA f 0.2970869 0.01365740 0.1559604 0.9436715 +#> 3: 1826.25 bili 1 <NA> 0.2370689 0.01649324 0.1274960 0.8571112 +#> 4: 1826.25 bili 2 <NA> 0.2861763 0.04155204 0.1772219 0.8930122 +#> 5: 1826.25 bili 3 <NA> 0.3366275 0.06634432 0.2418444 0.9094943 +#> 6: 1826.25 bili 4 <NA> 0.3831676 0.09015452 0.3069383 0.9098886 +#> 7: 1826.25 bili 5 <NA> 0.4170359 0.12377812 0.3463979 0.9119499

    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.

    @@ -296,133 +296,133 @@

    Multiple variables, marginally#> #> |---------------- Risk ----------------| #> Value Mean Median 25th % 75th % -#> 0.80 0.2327184 0.1273228 0.05225254 0.3654963 -#> 1.4 0.2525347 0.1482291 0.06341848 0.3864174 -#> 3.5 0.3630145 0.2741614 0.15717347 0.5336957 +#> 0.80 0.2320650 0.1264348 0.05237649 0.3603578 +#> 1.4 0.2521245 0.1474908 0.06310080 0.3843744 +#> 3.5 0.3636256 0.2778900 0.15739236 0.5368623 #> #> -- copper (VI Rank: 2) ----------------------------------- #> #> |---------------- Risk ----------------| #> Value Mean Median 25th % 75th % -#> 43 0.2620408 0.1419786 0.05286547 0.4425476 -#> 74 0.2812392 0.1626328 0.06248756 0.4832891 -#> 129 0.3338460 0.2167987 0.10890100 0.5416276 +#> 43 0.2622133 0.1396244 0.05247912 0.4400439 +#> 74 0.2812345 0.1614353 0.06114537 0.4824855 +#> 129 0.3338021 0.2209719 0.10952284 0.5470571 #> #> -- sex (VI Rank: 3) -------------------------------------- #> #> |---------------- Risk ----------------| #> Value Mean Median 25th % 75th % -#> m 0.3450763 0.2341746 0.11323181 0.5729915 -#> f 0.2969945 0.1582882 0.05475297 0.5297609 +#> m 0.3447674 0.2348158 0.11100223 0.5739881 +#> f 0.2970869 0.1559604 0.05481122 0.5348545 #> -#> -- albumin (VI Rank: 4) ---------------------------------- +#> -- stage (VI Rank: 4) ------------------------------------ #> #> |---------------- Risk ----------------| #> Value Mean Median 25th % 75th % -#> 3.3 0.3180525 0.1898755 0.06583830 0.5610983 -#> 3.5 0.2923217 0.1614292 0.05509017 0.5247966 -#> 3.8 0.2753494 0.1470438 0.05141991 0.4831669 +#> 1 0.2564498 0.1341887 0.04878656 0.4321182 +#> 2 0.2678138 0.1395975 0.04865086 0.4632783 +#> 3 0.2920537 0.1589783 0.05262256 0.5070032 +#> 4 0.3362837 0.2073258 0.08081074 0.5770044 #> -#> -- stage (VI Rank: 5) ------------------------------------ +#> -- albumin (VI Rank: 5) ---------------------------------- #> #> |---------------- Risk ----------------| #> Value Mean Median 25th % 75th % -#> 1 0.2565169 0.1332526 0.04777084 0.4290594 -#> 2 0.2678461 0.1407078 0.04873690 0.4672301 -#> 3 0.2918513 0.1590607 0.05097333 0.5111181 -#> 4 0.3362603 0.2080870 0.08124797 0.5762128 +#> 3.3 0.3182502 0.1889076 0.06794074 0.5647898 +#> 3.5 0.2925933 0.1597826 0.05473953 0.5287585 +#> 3.8 0.2756437 0.1494309 0.05000052 0.4844053 #> #> -- protime (VI Rank: 6) ---------------------------------- #> #> |---------------- Risk ----------------| #> Value Mean Median 25th % 75th % -#> 10 0.2800703 0.1529021 0.05207510 0.4882585 -#> 11 0.2937525 0.1663129 0.05956678 0.5266687 -#> 11 0.3156797 0.1859504 0.07198972 0.5512110 +#> 10 0.2800359 0.1534317 0.05320390 0.4924859 +#> 11 0.2939533 0.1681997 0.06030034 0.5232989 +#> 11 0.3156198 0.1871898 0.07128974 0.5519453 #> #> -- age (VI Rank: 7) -------------------------------------- #> #> |---------------- Risk ----------------| #> Value Mean Median 25th % 75th % -#> 42 0.2709078 0.1353262 0.05194180 0.4517125 -#> 50 0.2977417 0.1775336 0.05491761 0.5279416 -#> 57 0.3294981 0.2271520 0.07495986 0.5634550 +#> 42 0.2712562 0.1369091 0.05294650 0.4536555 +#> 50 0.2980184 0.1773983 0.05602234 0.5318555 +#> 57 0.3293389 0.2329844 0.07349113 0.5641154 #> #> -- ast (VI Rank: 8) -------------------------------------- #> #> |---------------- Risk ----------------| #> Value Mean Median 25th % 75th % -#> 82 0.2797975 0.1436339 0.05222653 0.4997200 -#> 117 0.2968602 0.1567258 0.05757446 0.5346486 -#> 153 0.3208992 0.1810402 0.06857810 0.5830423 +#> 82 0.2800294 0.1464464 0.05099136 0.5004018 +#> 117 0.2969289 0.1527369 0.05662912 0.5362550 +#> 153 0.3208015 0.1774712 0.06881978 0.5851786 #> #> -- ascites (VI Rank: 9) ---------------------------------- #> #> |---------------- Risk ----------------| #> Value Mean Median 25th % 75th % -#> 0 0.2969703 0.1652904 0.05544813 0.5192320 -#> 1 0.4607430 0.3916449 0.25763892 0.6487705 +#> 0 0.2970368 0.1634787 0.05543517 0.5229307 +#> 1 0.4607350 0.3968513 0.25475163 0.6483474 #> #> -- chol (VI Rank: 10) ------------------------------------ #> #> |---------------- Risk ----------------| #> Value Mean Median 25th % 75th % -#> 250 0.2854741 0.1510147 0.04777092 0.4980829 -#> 310 0.2934727 0.1592817 0.05416948 0.5002304 -#> 401 0.3147478 0.1879679 0.07537050 0.5189118 +#> 250 0.2854925 0.1509357 0.04722530 0.4946851 +#> 310 0.2935160 0.1591265 0.05308077 0.4988662 +#> 401 0.3149044 0.1886952 0.07599708 0.5149088 #> #> -- hepato (VI Rank: 11) ---------------------------------- #> #> |---------------- Risk ----------------| #> Value Mean Median 25th % 75th % -#> 0 0.2854951 0.1584528 0.05153518 0.5029106 -#> 1 0.3181661 0.1841264 0.06951905 0.5558180 +#> 0 0.2857662 0.1584305 0.05212068 0.5018660 +#> 1 0.3182069 0.1800537 0.07116109 0.5619836 #> #> -- edema (VI Rank: 12) ----------------------------------- #> #> |---------------- Risk ----------------| #> Value Mean Median 25th % 75th % -#> 0 0.2926769 0.1617680 0.05508989 0.5162688 -#> 0.5 0.3514656 0.2454979 0.10627466 0.5876935 -#> 1 0.4545362 0.3570984 0.26257516 0.6397581 +#> 0 0.2926830 0.1600182 0.05496315 0.5190108 +#> 0.5 0.3517272 0.2445877 0.10376815 0.5863452 +#> 1 0.4551010 0.3588004 0.26204287 0.6419355 #> #> -- spiders (VI Rank: 13) --------------------------------- #> #> |---------------- Risk ----------------| #> Value Mean Median 25th % 75th % -#> 0 0.2913381 0.1518586 0.05198324 0.5115191 -#> 1 0.3317790 0.2101885 0.08651576 0.5612115 +#> 0 0.2913187 0.1503019 0.05371979 0.5105295 +#> 1 0.3317084 0.2117035 0.08667978 0.5632173 #> #> -- trt (VI Rank: 14) ------------------------------------- #> #> |---------------- Risk ----------------| #> Value Mean Median 25th % 75th % -#> d_penicill_main 0.3060255 0.1706895 0.06010306 0.5297609 -#> placebo 0.3017811 0.1606578 0.05389244 0.5535583 +#> d_penicill_main 0.3059387 0.1732905 0.05956365 0.5330342 +#> placebo 0.3018217 0.1577341 0.05390522 0.5508407 #> #> -- trig (VI Rank: 15) ------------------------------------ #> #> |---------------- Risk ----------------| #> Value Mean Median 25th % 75th % -#> 85 0.2916492 0.1548444 0.05141010 0.5128944 -#> 108 0.3002657 0.1634343 0.05292353 0.5248711 -#> 151 0.3149828 0.1836690 0.06048690 0.5230871 +#> 85 0.2915820 0.1529553 0.05105095 0.5075746 +#> 108 0.3003222 0.1623495 0.05343192 0.5213774 +#> 151 0.3148633 0.1803912 0.06151820 0.5204135 #> #> -- alk.phos (VI Rank: 16) -------------------------------- #> #> |---------------- Risk ----------------| #> Value Mean Median 25th % 75th % -#> 922 0.3027347 0.1639213 0.05771319 0.5503003 -#> 1278 0.3036075 0.1631731 0.05844479 0.5505550 -#> 2068 0.3075619 0.1652061 0.06086602 0.5565652 +#> 922 0.3029278 0.1650884 0.05647195 0.5532766 +#> 1278 0.3036634 0.1668115 0.05729337 0.5544986 +#> 2068 0.3075824 0.1678166 0.05974829 0.5602714 #> #> -- platelet (VI Rank: 17) -------------------------------- #> #> |---------------- Risk ----------------| #> Value Mean Median 25th % 75th % -#> 200 0.3080425 0.1685648 0.05811024 0.5635815 -#> 257 0.3021727 0.1615794 0.05495377 0.5526327 -#> 318 0.2975173 0.1605372 0.05722051 0.5341292 +#> 200 0.3079889 0.1683616 0.05720281 0.5628406 +#> 257 0.3020654 0.1604983 0.05440290 0.5565127 +#> 318 0.2974147 0.1601360 0.05500888 0.5364649 #> #> Predicted risk at time t = 1826.25 for top 17 predictors

    It’s easy enough to turn this ‘summary’ object into a @@ -431,12 +431,12 @@

    Multiple variables, marginally head(as.data.table(pd_smry)) #> variable importance Value Mean Median 25th % 75th % -#> 1: bili 0.12476840 0.80 0.2327184 0.1273228 0.05225254 0.3654963 -#> 2: bili 0.12476840 1.4 0.2525347 0.1482291 0.06341848 0.3864174 -#> 3: bili 0.12476840 3.5 0.3630145 0.2741614 0.15717347 0.5336957 -#> 4: copper 0.05293328 43 0.2620408 0.1419786 0.05286547 0.4425476 -#> 5: copper 0.05293328 74 0.2812392 0.1626328 0.06248756 0.4832891 -#> 6: copper 0.05293328 129 0.3338460 0.2167987 0.10890100 0.5416276 +#> 1: bili 0.12460516 0.80 0.2320650 0.1264348 0.05237649 0.3603578 +#> 2: bili 0.12460516 1.4 0.2521245 0.1474908 0.06310080 0.3843744 +#> 3: bili 0.12460516 3.5 0.3636256 0.2778900 0.15739236 0.5368623 +#> 4: copper 0.05317731 43 0.2622133 0.1396244 0.05247912 0.4400439 +#> 5: copper 0.05317731 74 0.2812345 0.1614353 0.06114537 0.4824855 +#> 6: copper 0.05317731 129 0.3338021 0.2209719 0.10952284 0.5470571 #> pred_horizon level #> 1: 1826.25 <NA> #> 2: 1826.25 <NA> @@ -532,17 +532,17 @@

    Visualizing ICE curves ice_oob #> id_variable id_row pred_horizon bili pred -#> 1: 1 1 1826.25 1 0.87570772 -#> 2: 1 2 1826.25 1 0.11154785 -#> 3: 1 3 1826.25 1 0.70858286 -#> 4: 1 4 1826.25 1 0.33712379 -#> 5: 1 5 1826.25 1 0.09915492 +#> 1: 1 1 1826.25 1 0.87600481 +#> 2: 1 2 1826.25 1 0.11246943 +#> 3: 1 3 1826.25 1 0.71436728 +#> 4: 1 4 1826.25 1 0.32912166 +#> 5: 1 5 1826.25 1 0.09966635 #> --- -#> 6896: 25 272 1826.25 10 0.27692870 -#> 6897: 25 273 1826.25 10 0.45987576 -#> 6898: 25 274 1826.25 10 0.47003465 +#> 6896: 25 272 1826.25 10 0.27390916 +#> 6897: 25 273 1826.25 10 0.46054140 +#> 6898: 25 274 1826.25 10 0.46097024 #> 6899: 25 275 1826.25 10 0.28302651 -#> 6900: 25 276 1826.25 10 0.54985041 +#> 6900: 25 276 1826.25 10 0.55279665