Running ‘testthat.R’ [428s/518s]
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
  
  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