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> library(testthat)
> library(ptLasso)
Loading required package: ggplot2
Loading required package: glmnet
Loading required package: Matrix
Loaded glmnet 4.1-10
Loading required package: gridExtra
> 
> test_check("ptLasso")

Call:  
cv.ptLasso(x = x, y = y, groups = groups, family = "cox", type.measure = "C",  
    nfolds = 5, foldid = NULL, overall.lambda = "lambda.min",  
    use.case = "inputGroups", group.intercepts = TRUE) 



type.measure:  C 


           alpha overall   mean wtdMean group_1 group_2 group_3 group_4 group_5
Overall           0.6054 0.5874  0.6020  0.6116  0.5842  0.6825  0.5179  0.5405
Pretrain     0.0  0.5807 0.6582  0.6459  0.6049  0.6475  0.6766  0.5799  0.7822
Pretrain     0.1  0.5733 0.6192  0.6157  0.5833  0.5870  0.7214  0.5433  0.6611
Pretrain     0.2  0.6333 0.6841  0.6928  0.6868  0.7298  0.6889  0.7333  0.5816
Pretrain     0.3  0.6009 0.6277  0.6488  0.6813  0.6707  0.6413  0.5731  0.5723
Pretrain     0.4  0.6182 0.6342  0.6438  0.6415  0.6667  0.6586  0.6766  0.5275
Pretrain     0.5  0.6575 0.6984  0.7015  0.6634  0.7450  0.7335  0.7200  0.6303
Pretrain     0.6  0.6270 0.6685  0.6660  0.6359  0.6644  0.7262  0.6700  0.6462
Pretrain     0.7  0.5757 0.6163  0.6261  0.6987  0.5970  0.5540  0.5213  0.7105
Pretrain     0.8  0.6069 0.5946  0.6194  0.6819  0.6186  0.5987  0.6000  0.4740
Pretrain     0.9  0.6399 0.6380  0.6533  0.6959  0.6922  0.5659  0.6174  0.6184
Pretrain     1.0  0.6164 0.6418  0.6634  0.7337  0.6412  0.6447  0.5981  0.5911
Individual        0.6164 0.6418  0.6634  0.7337  0.6412  0.6447  0.5981  0.5911

alphahat (fixed) = 0.5
alphahat (varying):
group_1 group_2 group_3 group_4 group_5 
    1.0     0.5     0.5     0.2     0.0 

Call:  
cv.ptLassoMult(x = x, y = y, alphalist = alphalist, type.measure = "mae",  
    nfolds = nfolds, foldid = foldid, verbose = verbose, fitoverall = fitoverall,  
    fitind = fitind, s = s, gamma = gamma, call = this.call,  
    use.case = "multiresponse", group.intercepts = FALSE) 



type.measure:  mae 


           alpha overall  mean response_1 response_2
Overall            3.413 1.707      1.693      1.720
Pretrain     0.0   3.324 1.662      1.653      1.671
Pretrain     0.1   3.311 1.656      1.648      1.663
Pretrain     0.2   3.304 1.652      1.646      1.658
Pretrain     0.3   3.307 1.654      1.648      1.660
Pretrain     0.4   3.319 1.660      1.653      1.667
Pretrain     0.5   3.335 1.667      1.659      1.676
Pretrain     0.6   3.354 1.677      1.666      1.687
Pretrain     0.7   3.375 1.688      1.675      1.700
Pretrain     0.8   3.397 1.698      1.684      1.713
Pretrain     0.9   3.418 1.709      1.693      1.725
Pretrain     1.0   3.438 1.719      1.702      1.736
Individual         3.438 1.719      1.702      1.736

alphahat (fixed) = 0.2
alphahat (varying):
response_1 response_2 
       0.2        0.2 

Call:  
cv.ptLasso(x = x, y = y, groups = groups, family = "multinomial",  
    use.case = "targetGroups", type.measure = "class", nfolds = 3,  
    foldid = NULL, overall.lambda = "lambda.min", group.intercepts = TRUE) 



type.measure:  class 


           alpha overall    mean group_1 group_2 group_3
Overall          0.02667                                
Pretrain     0.0 0.03000 0.04333 0.03667 0.04667 0.04667
Pretrain     0.1 0.04000 0.04444 0.04667 0.04000 0.04667
Pretrain     0.2 0.04333 0.04000 0.03667 0.03333 0.05000
Pretrain     0.3 0.05000 0.04000 0.03333 0.04667 0.04000
Pretrain     0.4 0.04333 0.03889 0.02667 0.03667 0.05333
Pretrain     0.5 0.04667 0.04222 0.04667 0.03667 0.04333
Pretrain     0.6 0.03333 0.04222 0.03667 0.05000 0.04000
Pretrain     0.7 0.03667 0.04889 0.04333 0.05333 0.05000
Pretrain     0.8 0.05000 0.04778 0.04000 0.04333 0.06000
Pretrain     0.9 0.04333 0.04333 0.03667 0.04000 0.05333
Pretrain     1.0 0.02333 0.04222 0.03333 0.05000 0.04333
Individual       0.02333 0.04222 0.03333 0.05000 0.04333

alphahat (fixed) = 1
alphahat (varying):
group_1 group_2 group_3 
    0.4     0.2     0.3 
[ FAIL 0 | WARN 12 | SKIP 0 | PASS 123 ]

[ FAIL 0 | WARN 12 | SKIP 0 | PASS 123 ]
> 
> proc.time()
   user  system elapsed 
 499.60    9.37  508.96