R Under development (unstable) (2023-08-26 r85014 ucrt) -- "Unsuffered Consequences"
Copyright (C) 2023 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> # This file is part of the standard setup for testthat.
> # It is recommended that you do not modify it.
> #
> # Where should you do additional test configuration?
> # Learn more about the roles of various files in:
> # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
> # * https://testthat.r-lib.org/articles/special-files.html
> 
> library(testthat)
> library(VMDML)
> 
> test_check("VMDML")

Call:
 randomForest(formula = yt ~ ., data = traindata, mtry = m, ntree = n) 
               Type of random forest: regression
                     Number of trees: 5
No. of variables tried at each split: 1

          Mean of squared residuals: 8.316385e-05
                    % Var explained: 96.06

Call:
 randomForest(formula = yt ~ ., data = traindata, mtry = m, ntree = n) 
               Type of random forest: regression
                     Number of trees: 5
No. of variables tried at each split: 1

          Mean of squared residuals: 0.01349872
                    % Var explained: -17.52

Call:
 randomForest(formula = yt ~ ., data = traindata, mtry = m, ntree = n) 
               Type of random forest: regression
                     Number of trees: 5
No. of variables tried at each split: 1

          Mean of squared residuals: 0.01559752
                    % Var explained: -19.35

Call:
svm(formula = yt ~ ., data = traindata, kernel = ker.funct, type = svm.type)


Parameters:
   SVM-Type:  nu-regression 
 SVM-Kernel:  radial 
       cost:  1 
         nu:  0.5 

Number of Support Vectors:  124


Call:
svm(formula = yt ~ ., data = traindata, kernel = ker.funct, type = svm.type)


Parameters:
   SVM-Type:  nu-regression 
 SVM-Kernel:  radial 
       cost:  1 
         nu:  0.5 

Number of Support Vectors:  123


Call:
svm(formula = yt ~ ., data = traindata, kernel = ker.funct, type = svm.type)


Parameters:
   SVM-Type:  nu-regression 
 SVM-Kernel:  radial 
       cost:  1 
         nu:  0.5 

Number of Support Vectors:  124

[ FAIL 0 | WARN 3 | SKIP 0 | PASS 0 ]

[ FAIL 0 | WARN 3 | SKIP 0 | PASS 0 ]
> 
> proc.time()
   user  system elapsed 
  14.96    0.71   15.67