CRAN Package Check Results for Package personalized

Last updated on 2020-02-21 22:51:15 CET.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.2.5 19.45 539.68 559.13 OK
r-devel-linux-x86_64-debian-gcc 0.2.5 16.88 404.11 420.99 OK
r-devel-linux-x86_64-fedora-clang 0.2.5 647.67 OK
r-devel-linux-x86_64-fedora-gcc 0.2.5 636.83 OK
r-devel-windows-ix86+x86_64 0.2.5 39.00 186.00 225.00 OK --no-examples --no-tests --no-vignettes
r-devel-windows-ix86+x86_64-gcc8 0.2.5 56.00 123.00 179.00 OK --no-examples --no-tests --no-vignettes
r-patched-linux-x86_64 0.2.5 OK
r-patched-solaris-x86 0.2.5 1010.90 ERROR
r-release-linux-x86_64 0.2.5 17.14 503.96 521.10 OK
r-release-windows-ix86+x86_64 0.2.5 30.00 127.00 157.00 OK --no-examples --no-tests --no-vignettes
r-release-osx-x86_64 0.2.5 OK
r-oldrel-windows-ix86+x86_64 0.2.5 22.00 122.00 144.00 OK --no-examples --no-tests --no-vignettes
r-oldrel-osx-x86_64 0.2.5 OK

Check Details

Version: 0.2.5
Check: tests
Result: ERROR
     Running ‘testthat.R’ [430s/480s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > Sys.setenv("R_TESTS" = "")
     > library(testthat)
     > library(personalized)
     Loading required package: glmnet
     Loading required package: Matrix
     Loaded glmnet 3.0-2
    
     Loading required package: mgcv
     Loading required package: nlme
     This is mgcv 1.8-31. For overview type 'help("mgcv-package")'.
     Loading required package: gbm
     Loaded gbm 2.1.5
     Loading required package: ggplot2
     Loading required package: plotly
    
     Attaching package: 'plotly'
    
     The following object is masked from 'package:ggplot2':
    
     last_plot
    
     The following object is masked from 'package:stats':
    
     filter
    
     The following object is masked from 'package:graphics':
    
     layout
    
     >
     > test_check("personalized")
     family: gaussian
     loss: sq_loss_lasso
     method: weighting
     cutpoint: 0
     propensity
     function: propensity.func
    
     benefit score: f(x),
     Trt recom = 1*I(f(x)>c)+0*I(f(x)<=c) where c is 'cutpoint'
    
     Average Outcomes:
     Recommended 0 Recommended 1
     Received 0 8.9342 (n = 24) -9.2993 (n = 17)
     Received 1 -5.888 (n = 29) 7.2975 (n = 30)
    
     Treatment effects conditional on subgroups:
     Est of E[Y|T=0,Recom=0]-E[Y|T=/=0,Recom=0]
     14.8221 (n = 53)
     Est of E[Y|T=1,Recom=1]-E[Y|T=/=1,Recom=1]
     16.5969 (n = 47)
    
     NOTE: The above average outcomes are biased estimates of
     the expected outcomes conditional on subgroups.
     Use 'validate.subgroup()' to obtain unbiased estimates.
    
     ---------------------------------------------------
    
     Benefit score quantiles (f(X) for 1 vs 0):
     0% 25% 50% 75% 100%
     -21.3320 -6.0504 -0.6541 5.9764 24.5545
    
     ---------------------------------------------------
    
     Summary of individual treatment effects:
     E[Y|T=1, X] - E[Y|T=0, X]
    
     Min. 1st Qu. Median Mean 3rd Qu. Max.
     -42.66391 -12.10080 -1.30820 0.03914 11.95277 49.10895
     family: gaussian
     loss: sq_loss_lasso
     method: weighting
     cutpoint: 0
     propensity
     function: propensity.func
    
     benefit score: f(x),
     Trt recom = 1*I(f(x)<c)+0*I(f(x)>=c) where c is 'cutpoint'
    
     Average Outcomes:
     Recommended 0 Recommended 1
     Received 0 -9.1702 (n = 17) 8.2552 (n = 24)
     Received 1 6.9478 (n = 33) -6.3978 (n = 26)
    
     Treatment effects conditional on subgroups:
     Est of E[Y|T=0,Recom=0]-E[Y|T=/=0,Recom=0]
     -16.118 (n = 50)
     Est of E[Y|T=1,Recom=1]-E[Y|T=/=1,Recom=1]
     -14.6529 (n = 50)
    
     NOTE: The above average outcomes are biased estimates of
     the expected outcomes conditional on subgroups.
     Use 'validate.subgroup()' to obtain unbiased estimates.
    
     ---------------------------------------------------
    
     Benefit score quantiles (f(X) for 1 vs 0):
     0% 25% 50% 75% 100%
     -19.3497 -5.4315 -0.2241 5.7181 22.5921
    
     ---------------------------------------------------
    
     Summary of individual treatment effects:
     E[Y|T=1, X] - E[Y|T=0, X]
    
     Min. 1st Qu. Median Mean 3rd Qu. Max.
     -38.6994 -10.8629 -0.4482 0.2176 11.4361 45.1843
     ── 1. Error: test fit.subgroup for continuous outcomes and multiple trts and var
     'NA' indices are not (yet?) supported for sparse Matrices
     Backtrace:
     1. personalized::fit.subgroup(...)
     2. personalized:::propensity.func(x = x, trt = trt)
     3. glmnet::cv.glmnet(y = trt, x = x, family = "multinomial")
     4. glmnet:::cv.glmnet.raw(...)
     6. glmnet:::buildPredmat.multnetlist(...)
     7. glmnet:::buildPredmat.array(...)
     9. glmnet:::predict.multnet(...)
     11. kbeta[, lamlist$left, drop = FALSE]
     12. Matrix:::subCsp_cols(x, j, drop = drop)
     13. Matrix:::intI(j, n = x@Dim[2], dn[[2]], give.dn = FALSE)
    
     Summary of individual treatment effects:
     E[Y|T=1, X] - E[Y|T=0, X]
    
     Min. 1st Qu. Median Mean 3rd Qu. Max.
     -52.214 -12.886 -1.902 -1.277 9.657 45.709
     Summary of individual treatment effects:
     E[Y|T=1, X] - E[Y|T=0, X]
    
     Min. 1st Qu. Median Mean 3rd Qu. Max.
     -31.318 -5.217 1.802 2.261 9.616 33.776
     Summary of individual treatment effects:
     E[Y|T=1, X] - E[Y|T=0, X]
    
     Min. 1st Qu. Median Mean 3rd Qu. Max.
     -31.318 -5.217 1.802 2.261 9.616 33.776
     Summary of individual treatment effects:
     E[Y|T=1, X] / E[Y|T=0, X]
    
     Note: for survival outcomes, the above ratio is
     E[g(Y)|T=1, X] / E[g(Y)|T=0, X],
     where g() is a monotone increasing function of Y,
     the survival time
    
     Min. 1st Qu. Median Mean 3rd Qu. Max.
     0.2158 0.7897 1.1183 1.2604 1.5708 4.1918
     family: binomial
     loss: logistic_loss_lasso
     method: weighting
    
     validation method: training_test_replication
     cutpoint: 0
     replications: 3
    
     benefit score: f(x),
     Trt recom = 1*I(f(x)>c)+0*I(f(x)<=c) where c is 'cutpoint'
    
     Average Test Set Outcomes:
     Recommended 0 Recommended 1
     Received 0 1 (SE = 0, n = 3.6667) 0.0424 (SE = 0.0735, n = 5.6667)
     Received 1 0.0331 (SE = 0.0573, n = 7.3333) 0.9339 (SE = 0.1145, n = 8.3333)
    
     Treatment effects conditional on subgroups:
     Est of E[Y|T=0,Recom=0]-E[Y|T=/=0,Recom=0]
     0.9669 (SE = 0.0573, n = 11)
     Est of E[Y|T=1,Recom=1]-E[Y|T=/=1,Recom=1]
     0.8915 (SE = 0.188, n = 14)
    
     Est of
     E[Y|Trt received = Trt recom] - E[Y|Trt received =/= Trt recom]:
     0.9067 (SE = 0.1213)
     family: gaussian
     loss: sq_loss_lasso
     method: weighting
    
     validation method: training_test_replication
     cutpoint: 0
     replications: 3
    
     benefit score: f(x),
     Trt recom = 1*I(f(x)<c)+0*I(f(x)>=c) where c is 'cutpoint'
    
     Average Test Set Outcomes:
     Recommended 0 Recommended 1
     Received 0 -27.101 (SE = 5.5353, n = 2.6667) 21.9228 (SE = 4.1631, n = 7.6667)
     Received 1 12.7535 (SE = 0.9564, n = 8.3333) -12.2184 (SE = 8.1861, n = 6.3333)
    
     Treatment effects conditional on subgroups:
     Est of E[Y|T=0,Recom=0]-E[Y|T=/=0,Recom=0]
     -39.8545 (SE = 4.9922, n = 11)
     Est of E[Y|T=1,Recom=1]-E[Y|T=/=1,Recom=1]
     -34.1413 (SE = 12.2768, n = 14)
    
     Est of
     E[Y|Trt received = Trt recom] - E[Y|Trt received =/= Trt recom]:
     -37.9764 (SE = 3.9789)
     family: binomial
     loss: logistic_loss_lasso
     method: weighting
    
     validation method: training_test_replication
     cutpoint: Quant_67
     replications: 3
    
     benefit score: f(x),
     Trt recom = 1*I(f(x)>c)+0*I(f(x)<=c) where c is 'cutpoint'
    
     Average Test Set Outcomes:
     Recommended 0 Recommended 1
     Received 0 0.8368 (SE = 0.1671, n = 5) 0 (SE = 0, n = 4.3333)
     Received 1 0.3433 (SE = 0.0485, n = 12) 1 (SE = 0, n = 3.6667)
    
     Treatment effects conditional on subgroups:
     Est of E[Y|T=0,Recom=0]-E[Y|T=/=0,Recom=0]
     0.4936 (SE = 0.1994, n = 17)
     Est of E[Y|T=1,Recom=1]-E[Y|T=/=1,Recom=1]
     1 (SE = 0, n = 8)
    
     Est of E[Y|Trt received = Trt recom] - E[Y|Trt received =/= Trt recom]:
     0.6946 (SE = 0.1454)
    
     <===============================================>
    
     family: binomial
     loss: logistic_loss_lasso
     method: weighting
    
     validation method: training_test_replication
     cutpoint: Quant_83
     replications: 3
    
     benefit score: f(x),
     Trt recom = 1*I(f(x)>c)+0*I(f(x)<=c) where c is 'cutpoint'
    
     Average Test Set Outcomes:
     Recommended 0 Recommended 1
     Received 0 0.6013 (SE = 0.1615, n = 6) 0 (SE = 0, n = 3.3333)
     Received 1 0.4436 (SE = 0.0868, n = 14) 1 (SE = 0, n = 1.6667)
    
     Treatment effects conditional on subgroups:
     Est of E[Y|T=0,Recom=0]-E[Y|T=/=0,Recom=0]
     0.1578 (SE = 0.1673, n = 20)
     Est of E[Y|T=1,Recom=1]-E[Y|T=/=1,Recom=1]
     1 (SE = 0, n = 5)
    
     Est of E[Y|Trt received = Trt recom] - E[Y|Trt received =/= Trt recom]:
     0.3363 (SE = 0.1617)
     family: binomial
     loss: logistic_loss_lasso
     method: weighting
    
     validation method: training_test_replication
     cutpoint: Quant_67
     replications: 3
    
     benefit score: f(x),
     Trt recom = 1*I(f(x)>c)+0*I(f(x)<=c) where c is 'cutpoint'
    
     Average Test Set Outcomes:
     Recommended 0 Recommended 1
     Received 0 0.8368 (SE = 0.1671, 20%) 0 (SE = 0, 17.3333%)
     Received 1 0.3433 (SE = 0.0485, 48%) 1 (SE = 0, 14.6667%)
    
     Treatment effects conditional on subgroups:
     Est of E[Y|T=0,Recom=0]-E[Y|T=/=0,Recom=0]
     0.4936 (SE = 0.1994, 68%)
     Est of E[Y|T=1,Recom=1]-E[Y|T=/=1,Recom=1]
     1 (SE = 0, 32%)
    
     Est of E[Y|Trt received = Trt recom] - E[Y|Trt received =/= Trt recom]:
     0.6946 (SE = 0.1454)
    
     <===============================================>
    
     family: binomial
     loss: logistic_loss_lasso
     method: weighting
    
     validation method: training_test_replication
     cutpoint: Quant_83
     replications: 3
    
     benefit score: f(x),
     Trt recom = 1*I(f(x)>c)+0*I(f(x)<=c) where c is 'cutpoint'
    
     Average Test Set Outcomes:
     Recommended 0 Recommended 1
     Received 0 0.6013 (SE = 0.1615, 24%) 0 (SE = 0, 13.3333%)
     Received 1 0.4436 (SE = 0.0868, 56%) 1 (SE = 0, 6.6667%)
    
     Treatment effects conditional on subgroups:
     Est of E[Y|T=0,Recom=0]-E[Y|T=/=0,Recom=0]
     0.1578 (SE = 0.1673, 80%)
     Est of E[Y|T=1,Recom=1]-E[Y|T=/=1,Recom=1]
     1 (SE = 0, 20%)
    
     Est of E[Y|Trt received = Trt recom] - E[Y|Trt received =/= Trt recom]:
     0.3363 (SE = 0.1617)
     family: cox
     loss: cox_loss_lasso
     method: weighting
    
     validation method: training_test_replication
     cutpoint: 0
     replications: 3
    
     benefit score: f(x),
     Trt recom = 1*I(f(x)>c)+0*I(f(x)<=c) where c is 'cutpoint'
    
     Average Test Set Outcomes:
     Recommended 0 Recommended 1
     Received 0 24.4288 (SE = 30.2323, n = 4) 0 (SE = 0, n = 4.6667)
     Received 1 0 (SE = 0, n = 7.6667) 1.1995 (SE = 0.7975, n = 8.6667)
    
     Treatment effects conditional on subgroups:
     Est of E[Y|T=0,Recom=0]-E[Y|T=/=0,Recom=0]
     24.4288 (SE = 30.2323, n = 11.6667)
     Est of E[Y|T=1,Recom=1]-E[Y|T=/=1,Recom=1]
     1.1995 (SE = 0.7975, n = 13.3333)
    
     Est of
     E[Y|Trt received = Trt recom] - E[Y|Trt received =/= Trt recom]:
     14.7452 (SE = 12.9127)
     family: cox
     loss: cox_loss_lasso
     method: weighting
    
     validation method: boot_bias_correction
     cutpoint: 0
     replications: 3
    
     benefit score: f(x),
     Trt recom = 1*I(f(x)>c)+0*I(f(x)<=c) where c is 'cutpoint'
    
     Average Bootstrap Bias-Corrected Outcomes:
     Recommended 0 Recommended 1
     Received 0 31.5529 (SE = 16.8759, n = 17.3333) 0 (SE = 0, n = 25.6667)
     Received 1 0 (SE = 0, n = 31.6667) 1.8959 (SE = 0.891, n = 25.3333)
    
     Treatment effects conditional on subgroups:
     Est of E[Y|T=0,Recom=0]-E[Y|T=/=0,Recom=0]
     31.5529 (SE = 16.8759, n = 49)
     Est of E[Y|T=1,Recom=1]-E[Y|T=/=1,Recom=1]
     1.8959 (SE = 0.891, n = 51)
    
     Est of
     E[Y|Trt received = Trt recom] - E[Y|Trt received =/= Trt recom]:
     34.3274 (SE = 11.4573)
     family: gaussian
     loss: sq_loss_lasso
     method: weighting
     cutpoint: 0
     propensity
     function: propensity.func
    
     benefit score: f_2(x): 2 vs 1, f_3(x): 3 vs 1
     f_1(x): 0
     maxval = max(f_2(x), f_3(x))
     which.max(maxval) = The trt level which maximizes maxval
     Trt recom = which.max(maxval)*I(maxval > c) + 1*I(maxval <= c) where c is 'cutpoint'
    
     Average Outcomes:
     Recommended 1 Recommended 2 Recommended 3
     Received 1 22.9689 (n = 5) 17.4287 (n = 26) -6.2399 (n = 2)
     Received 2 -13.9114 (n = 2) 34.3338 (n = 4) -13.4677 (n = 36)
     Received 3 -27.2658 (n = 8) -38.4163 (n = 5) 31.1159 (n = 12)
    
     Treatment effects conditional on subgroups:
     Est of E[Y|T=1,Recom=1]-E[Y|T=/=1,Recom=1]
     45.4931 (n = 15)
     Est of E[Y|T=2,Recom=2]-E[Y|T=/=2,Recom=2]
     30.8919 (n = 35)
     Est of E[Y|T=3,Recom=3]-E[Y|T=/=3,Recom=3]
     44.0447 (n = 50)
    
     NOTE: The above average outcomes are biased estimates of
     the expected outcomes conditional on subgroups.
     Use 'validate.subgroup()' to obtain unbiased estimates.
    
     ---------------------------------------------------
    
     Benefit score 1 quantiles (f(X) for 2 vs 1):
     0% 25% 50% 75% 100%
     -42.739 -16.245 -2.885 10.195 52.217
    
     Benefit score 2 quantiles (f(X) for 3 vs 1):
     0% 25% 50% 75% 100%
     -92.51 -22.80 1.36 28.52 88.63
    
     ---------------------------------------------------
    
     Summary of individual treatment effects:
     E[Y|T=trt, X] - E[Y|T=1, X]
     where 'trt' is 2 and 3
    
     2-vs-1 3-vs-1
     Min. :-85.479 Min. :-185.013
     1st Qu.:-32.490 1st Qu.: -45.600
     Median : -5.771 Median : 2.720
     Mean : -3.105 Mean : 3.232
     3rd Qu.: 20.390 3rd Qu.: 57.047
     Max. :104.433 Max. : 177.255
     family: gaussian
     loss: sq_loss_lasso
     method: weighting
    
     validation method: training_test_replication
     cutpoint: 0
     replications: 2
    
     benefit score: f_2(x): 2 vs 1, f_3(x): 3 vs 1
     f_1(x): 0
     maxval = max(f_2(x), f_3(x))
     which.max(maxval) = The trt level which maximizes maxval
     Trt recom = which.max(maxval)*I(maxval > c) + 1*I(maxval <= c) where c is 'cutpoint'
    
     Average Test Set Outcomes:
     Recommended 1 Recommended 2
     Received 1 10.9365 (SE = 18.2443, n = 2) 15.0693 (SE = 11.5189, n = 6.5)
     Received 2 NaN (SE = NA, n = 0) 18.6765 (SE = NA, n = 1)
     Received 3 -25.872 (SE = 4.9554, n = 1.5) 17.1442 (SE = NA, n = 0.5)
     Recommended 3
     Received 1 23.9965 (SE = NA, n = 0.5)
     Received 2 -18.3166 (SE = 3.866, n = 9.5)
     Received 3 45.3389 (SE = 5.7727, n = 3.5)
    
     Treatment effects conditional on subgroups:
     Est of E[Y|T=1,Recom=1]-E[Y|T=/=1,Recom=1]
     36.8085 (SE = 23.1997, n = 3.5)
     Est of E[Y|T=2,Recom=2]-E[Y|T=/=2,Recom=2]
     11.7522 (SE = NA, n = 8)
     Est of E[Y|T=3,Recom=3]-E[Y|T=/=3,Recom=3]
     58.4252 (SE = 9.3034, n = 13.5)
    
     Est of
     E[Y|Trt received = Trt recom] - E[Y|Trt received =/= Trt recom]:
     39.3959 (SE = 13.392)
     family: gaussian
     loss: sq_loss_lasso
     method: weighting
    
     validation method: training_test_replication
     cutpoint: Quant_33
     replications: 2
    
     benefit score: f_2(x): 2 vs 1, f_3(x): 3 vs 1
     f_1(x): 0
     maxval = max(f_2(x), f_3(x))
     which.max(maxval) = The trt level which maximizes maxval
     Trt recom = which.max(maxval)*I(maxval > c) + 1*I(maxval <= c) where c is 'cutpoint'
    
     Average Test Set Outcomes:
     Recommended 1 Recommended 2
     Received 1 NaN (SE = NA, n = 0) 16.7838 (SE = 1.8647, n = 8.5)
     Received 2 NaN (SE = NA, n = 0) 18.6765 (SE = NA, n = 1)
     Received 3 -44.1777 (SE = NA, n = 0.5) 10.5345 (SE = NA, n = 1)
     Recommended 3
     Received 1 23.9965 (SE = NA, n = 0.5)
     Received 2 -18.3166 (SE = 3.866, n = 9.5)
     Received 3 43.4349 (SE = 3.0802, n = 4)
    
     Treatment effects conditional on subgroups:
     Est of E[Y|T=1,Recom=1]-E[Y|T=/=1,Recom=1]
     NaN (SE = NA, n = 0.5)
     Est of E[Y|T=2,Recom=2]-E[Y|T=/=2,Recom=2]
     3.2112 (SE = NA, n = 10.5)
     Est of E[Y|T=3,Recom=3]-E[Y|T=/=3,Recom=3]
     56.5212 (SE = 6.6108, n = 14)
    
     Est of E[Y|Trt received = Trt recom] - E[Y|Trt received =/= Trt recom]:
     39.5889 (SE = 4.6787)
    
     <===============================================>
    
     family: gaussian
     loss: sq_loss_lasso
     method: weighting
    
     validation method: training_test_replication
     cutpoint: Quant_67
     replications: 2
    
     benefit score: f_2(x): 2 vs 1, f_3(x): 3 vs 1
     f_1(x): 0
     maxval = max(f_2(x), f_3(x))
     which.max(maxval) = The trt level which maximizes maxval
     Trt recom = which.max(maxval)*I(maxval > c) + 1*I(maxval <= c) where c is 'cutpoint'
    
     Average Test Set Outcomes:
     Recommended 1 Recommended 2
     Received 1 17.8705 (SE = 2.6598, n = 4) 17.1043 (SE = 11.472, n = 5)
     Received 2 18.6765 (SE = NA, n = 1) NaN (SE = NA, n = 0)
     Received 3 -12.0197 (SE = 24.5456, n = 3.5) NaN (SE = NA, n = 0)
     Recommended 3
     Received 1 NaN (SE = NA, n = 0)
     Received 2 -18.3166 (SE = 3.866, n = 9.5)
     Received 3 52.8108 (SE = 4.7941, n = 2)
    
     Treatment effects conditional on subgroups:
     Est of E[Y|T=1,Recom=1]-E[Y|T=/=1,Recom=1]
     19.2468 (SE = 12.1534, n = 8.5)
     Est of E[Y|T=2,Recom=2]-E[Y|T=/=2,Recom=2]
     NaN (SE = NA, n = 5)
     Est of E[Y|T=3,Recom=3]-E[Y|T=/=3,Recom=3]
     71.1273 (SE = 8.6602, n = 11.5)
    
     Est of E[Y|Trt received = Trt recom] - E[Y|Trt received =/= Trt recom]:
     42.2847 (SE = 5.1894)
     family: gaussian
     loss: sq_loss_lasso
     method: weighting
     cutpoint: 0
     propensity
     function: propensity.func
    
     benefit score: f_2(x): 2 vs 1, f_3(x): 3 vs 1
     f_1(x): 0
     minval = min(f_2(x), f_3(x))
     which.min(minval) = The trt level which mininizes minval
     Trt recom = which.min(minval)*I(minval < c) + 1*I(minval >= c) where c is 'cutpoint'
    
     Average Outcomes:
     Recommended 1 Recommended 2 Recommended 3
     Received 1 -12.4319 (n = 3) 15.7334 (n = 2) 20.9 (n = 28)
     Received 2 16.5515 (n = 8) -23.5188 (n = 28) 24.3617 (n = 6)
     Received 3 41.8225 (n = 2) 18.1545 (n = 14) -39.4779 (n = 9)
    
     Treatment effects conditional on subgroups:
     Est of E[Y|T=1,Recom=1]-E[Y|T=/=1,Recom=1]
     -44.6999 (n = 13)
     Est of E[Y|T=2,Recom=2]-E[Y|T=/=2,Recom=2]
     -41.3895 (n = 44)
     Est of E[Y|T=3,Recom=3]-E[Y|T=/=3,Recom=3]
     -61.9119 (n = 43)
    
     NOTE: The above average outcomes are biased estimates of
     the expected outcomes conditional on subgroups.
     Use 'validate.subgroup()' to obtain unbiased estimates.
    
     ---------------------------------------------------
    
     Benefit score 1 quantiles (f(X) for 2 vs 1):
     0% 25% 50% 75% 100%
     -48.278 -17.381 -2.286 12.856 57.189
    
     Benefit score 2 quantiles (f(X) for 3 vs 1):
     0% 25% 50% 75% 100%
     -98.440 -27.986 2.755 31.750 98.201
    
     ---------------------------------------------------
    
     Summary of individual treatment effects:
     E[Y|T=trt, X] - E[Y|T=1, X]
     where 'trt' is 2 and 3
    
     2-vs-1 3-vs-1
     Min. :-96.556 Min. :-196.881
     1st Qu.:-34.761 1st Qu.: -55.973
     Median : -4.571 Median : 5.511
     Mean : -2.344 Mean : 2.672
     3rd Qu.: 25.712 3rd Qu.: 63.500
     Max. :114.379 Max. : 196.401
     family: gaussian
     loss: sq_loss_lasso
     method: weighting
    
     validation method: training_test_replication
     cutpoint: 0
     replications: 2
    
     benefit score: f_2(x): 2 vs 1, f_3(x): 3 vs 1
     f_1(x): 0
     minval = min(f_2(x), f_3(x))
     which.min(minval) = The trt level which minimizes minval
     Trt recom = which.min(minval)*I(minval < c) + 1*I(minval >= c) where c is 'cutpoint'
    
     Average Test Set Outcomes:
     Recommended 1 Recommended 2
     Received 1 -12.4319 (SE = NA, n = 1.5) NaN (SE = NA, n = 0)
     Received 2 21.007 (SE = 17.8902, n = 1.5) -24.8481 (SE = 3.3736, n = 7.5)
     Received 3 17.1442 (SE = 0, n = 1) 35.5173 (SE = 22.6333, n = 2.5)
     Recommended 3
     Received 1 24.4409 (SE = 4.4403, n = 7)
     Received 2 42.5672 (SE = NA, n = 1)
     Received 3 -43.2845 (SE = 7.6407, n = 3)
    
     Treatment effects conditional on subgroups:
     Est of E[Y|T=1,Recom=1]-E[Y|T=/=1,Recom=1]
     -31.3354 (SE = NA, n = 4)
     Est of E[Y|T=2,Recom=2]-E[Y|T=/=2,Recom=2]
     -60.3653 (SE = 26.0069, n = 10)
     Est of E[Y|T=3,Recom=3]-E[Y|T=/=3,Recom=3]
     -72.1813 (SE = 18.3825, n = 11)
    
     Est of
     E[Y|Trt received = Trt recom] - E[Y|Trt received =/= Trt recom]:
     -60.6921 (SE = 3.7457)
     family: gaussian
     loss: sq_loss_lasso
     method: weighting
    
     validation method: training_test_replication
     cutpoint: Quant_33
     replications: 2
    
     benefit score: f_2(x): 2 vs 1, f_3(x): 3 vs 1
     f_1(x): 0
     minval = min(f_2(x), f_3(x))
     which.min(minval) = The trt level which minimizes minval
     Trt recom = which.min(minval)*I(minval < c) + 1*I(minval >= c) where c is 'cutpoint'
    
     Average Test Set Outcomes:
     Recommended 1 Recommended 2
     Received 1 0.1205 (SE = 17.7517, n = 2.5) NaN (SE = NA, n = 0)
     Received 2 -2.8543 (SE = 0.4226, n = 3.5) -28.3557 (SE = 3.2631, n = 5.5)
     Received 3 31.1302 (SE = 15.1212, n = 2) 3.6383 (SE = 8.0984, n = 1.5)
     Recommended 3
     Received 1 27.5455 (SE = 8.8308, n = 6)
     Received 2 42.5672 (SE = NA, n = 1)
     Received 3 -43.2845 (SE = 7.6407, n = 3)
    
     Treatment effects conditional on subgroups:
     Est of E[Y|T=1,Recom=1]-E[Y|T=/=1,Recom=1]
     -23.4634 (SE = 33.2635, n = 8)
     Est of E[Y|T=2,Recom=2]-E[Y|T=/=2,Recom=2]
     -31.994 (SE = 4.8353, n = 7)
     Est of E[Y|T=3,Recom=3]-E[Y|T=/=3,Recom=3]
     -73.7928 (SE = 20.6616, n = 10)
    
     Est of E[Y|Trt received = Trt recom] - E[Y|Trt received =/= Trt recom]:
     -54.5619 (SE = 1.9297)
    
     <===============================================>
    
     family: gaussian
     loss: sq_loss_lasso
     method: weighting
    
     validation method: training_test_replication
     cutpoint: Quant_67
     replications: 2
    
     benefit score: f_2(x): 2 vs 1, f_3(x): 3 vs 1
     f_1(x): 0
     minval = min(f_2(x), f_3(x))
     which.min(minval) = The trt level which minimizes minval
     Trt recom = which.min(minval)*I(minval < c) + 1*I(minval >= c) where c is 'cutpoint'
    
     Average Test Set Outcomes:
     Recommended 1 Recommended 2
     Received 1 -8.8854 (SE = NA, n = 0.5) -9.6264 (SE = NA, n = 0.5)
     Received 2 NaN (SE = NA, n = 0) -18.496 (SE = 5.7219, n = 8.5)
     Received 3 NaN (SE = NA, n = 0) 28.6713 (SE = 12.9516, n = 3)
     Recommended 3
     Received 1 22.6124 (SE = 7.0262, n = 7.5)
     Received 2 40.7872 (SE = NA, n = 1.5)
     Received 3 -24.7794 (SE = 18.5295, n = 3.5)
    
     Treatment effects conditional on subgroups:
     Est of E[Y|T=1,Recom=1]-E[Y|T=/=1,Recom=1]
     NaN (SE = NA, n = 0.5)
     Est of E[Y|T=2,Recom=2]-E[Y|T=/=2,Recom=2]
     -45.0031 (SE = 15.613, n = 12)
     Est of E[Y|T=3,Recom=3]-E[Y|T=/=3,Recom=3]
     -51.4132 (SE = 5.8161, n = 12.5)
    
     Est of E[Y|Trt received = Trt recom] - E[Y|Trt received =/= Trt recom]:
     -48.7959 (SE = 5.4438)
    
     Hyperbolic Tangent kernel function.
     Hyperparameters : scale = 1 offset = 1
    
    
     C 1.0000 10.0000
     CV weighted accuracy 0.3839 0.3521
     ══ testthat results ═══════════════════════════════════════════════════════════
     [ OK: 219 | SKIPPED: 0 | WARNINGS: 795 | FAILED: 1 ]
     1. Error: test fit.subgroup for continuous outcomes and multiple trts and various losses (@test-fitsubgroup.R#1772)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-patched-solaris-x86