Last updated on 2020-09-29 03:48:28 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.2.5 | 20.15 | 879.06 | 899.21 | OK | |
| r-devel-linux-x86_64-debian-gcc | 0.2.5 | 15.80 | 640.47 | 656.27 | OK | |
| r-devel-linux-x86_64-fedora-clang | 0.2.5 | 1014.64 | OK | |||
| r-devel-linux-x86_64-fedora-gcc | 0.2.5 | 1031.00 | OK | |||
| r-devel-windows-ix86+x86_64 | 0.2.5 | 41.00 | 104.00 | 145.00 | OK | --no-examples --no-tests --no-vignettes |
| r-patched-linux-x86_64 | 0.2.5 | 16.97 | 823.35 | 840.32 | OK | |
| r-patched-solaris-x86 | 0.2.5 | 1447.20 | ERROR | |||
| r-release-linux-x86_64 | 0.2.5 | 18.03 | 843.12 | 861.15 | OK | |
| r-release-macos-x86_64 | 0.2.5 | OK | ||||
| r-release-windows-ix86+x86_64 | 0.2.5 | 41.00 | 136.00 | 177.00 | OK | --no-examples --no-tests --no-vignettes |
| r-oldrel-macos-x86_64 | 0.2.5 | OK | ||||
| r-oldrel-windows-ix86+x86_64 | 0.2.5 | 35.00 | 138.00 | 173.00 | OK | --no-examples --no-tests --no-vignettes |
Version: 0.2.5
Check: tests
Result: ERROR
Running ‘testthat.R’ [453s/464s]
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 4.0-2
Loading required package: mgcv
Loading required package: nlme
This is mgcv 1.8-33. For overview type 'help("mgcv-package")'.
Loading required package: gbm
Loaded gbm 2.1.8
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
CV: 1
CV: 2
CV: 1
CV: 2
CV: 3
CV: 4
CV: 5
CV: 1
CV: 2
CV: 1
CV: 2
CV: 3
CV: 4
CV: 5
CV: 1
CV: 2
CV: 1
CV: 2
CV: 3
CV: 4
CV: 5
CV: 1
CV: 2
CV: 3
CV: 4
CV: 5
── 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 11.5812 (SE = 1.2797, n = 9) -13.2198 (SE = 7.8726, n = 5.6667)
Treatment effects conditional on subgroups:
Est of E[Y|T=0,Recom=0]-E[Y|T=/=0,Recom=0]
-38.6822 (SE = 5.9513, n = 11.6667)
Est of E[Y|T=1,Recom=1]-E[Y|T=/=1,Recom=1]
-35.1426 (SE = 11.86, n = 13.3333)
Est of
E[Y|Trt received = Trt recom] - E[Y|Trt received =/= Trt recom]:
-38.559 (SE = 3.9663)
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 19.7513 (n = 4) 15.9236 (n = 28) 23.9965 (n = 1)
Received 2 -13.9114 (n = 2) 31.9898 (n = 6) -15.5207 (n = 34)
Received 3 -28.2337 (n = 5) -41.1735 (n = 6) 29.1472 (n = 14)
Treatment effects conditional on subgroups:
Est of E[Y|T=1,Recom=1]-E[Y|T=/=1,Recom=1]
41.5168 (n = 11)
Est of E[Y|T=2,Recom=2]-E[Y|T=/=2,Recom=2]
30.0126 (n = 40)
Est of E[Y|T=3,Recom=3]-E[Y|T=/=3,Recom=3]
41.6508 (n = 49)
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%
-52.419 -18.670 -1.928 13.652 61.772
Benefit score 2 quantiles (f(X) for 3 vs 1):
0% 25% 50% 75% 100%
-103.786 -30.816 3.594 34.700 105.366
---------------------------------------------------
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. :-104.838 Min. :-207.572
1st Qu.: -37.339 1st Qu.: -61.632
Median : -3.855 Median : 7.189
Mean : -1.162 Mean : 2.327
3rd Qu.: 27.303 3rd Qu.: 69.399
Max. : 123.544 Max. : 210.732
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) 23.9965 (n = 1) 20.0737 (n = 29)
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]
-42.1553 (n = 43)
Est of E[Y|T=3,Recom=3]-E[Y|T=/=3,Recom=3]
-61.4123 (n = 44)
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%
-52.058 -18.664 -2.026 13.390 61.142
Benefit score 2 quantiles (f(X) for 3 vs 1):
0% 25% 50% 75% 100%
-103.660 -30.787 3.412 34.372 104.861
---------------------------------------------------
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. :-104.116 Min. :-207.320
1st Qu.: -37.328 1st Qu.: -61.574
Median : -4.052 Median : 6.825
Mean : -1.279 Mean : 2.121
3rd Qu.: 26.780 3rd Qu.: 68.744
Max. : 122.285 Max. : 209.722
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 NaN (SE = NA, n = 0) NaN (SE = NA, n = 0)
Received 2 NaN (SE = NA, n = 0) -18.496 (SE = 5.7219, n = 8.5)
Received 3 NaN (SE = NA, n = 0) 35.5173 (SE = 22.6333, n = 2.5)
Recommended 3
Received 1 19.3597 (SE = 11.6262, n = 8.5)
Received 2 40.7872 (SE = NA, n = 1.5)
Received 3 -6.2096 (SE = 7.7322, 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)
Est of E[Y|T=2,Recom=2]-E[Y|T=/=2,Recom=2]
-54.0133 (SE = 28.3552, n = 11)
Est of E[Y|T=3,Recom=3]-E[Y|T=/=3,Recom=3]
-29.5908 (SE = 25.0456, n = 14)
Est of E[Y|Trt received = Trt recom] - E[Y|Trt received =/= Trt recom]:
-42.9246 (SE = 2.8594)
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: 815 | 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