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 13.67 0.67 14.32