R version 4.4.0 beta (2024-04-12 r86412 ucrt) -- "Puppy Cup" Copyright (C) 2024 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. > library(testthat) > library(CAST) > > test_check("CAST") Loading required package: ggplot2 Loading required package: lattice note: variables were not weighted either because no weights or model were given, no variable importance could be retrieved from the given model, or the model has a single feature. Check caret::varImp(model) note: No model and no CV folds were given. The DI threshold is therefore based on all training data note: variables were not weighted either because no weights or model were given, no variable importance could be retrieved from the given model, or the model has a single feature. Check caret::varImp(model) note: No model and no CV folds were given. The DI threshold is therefore based on all training data No trainDI provided. 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Computing AOA... Finished! No trainDI provided. 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Finished! No trainDI provided. 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Computing AOA... Finished! Note: multiCV=TRUE calculated new AOA threshold of 0.1329 Threshold is stored in the attributes, access with attr(error_model, 'AOA_threshold'). Please refere to examples and details for further information. Note: No increase in performance found using more than 4 variables Note: No increase in performance found using more than 5 variables Note: No increase in performance found using more than 4 variables [1] "model using Sepal.Length,Sepal.Width will be trained now..." note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 . [1] "maximum number of models that still need to be trained: 8" [1] "model using Sepal.Length,Petal.Length will be trained now..." note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 . [1] "maximum number of models that still need to be trained: 7" [1] "model using Sepal.Length,Petal.Width will be trained now..." note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 . [1] "maximum number of models that still need to be trained: 6" [1] "model using Sepal.Width,Petal.Length will be trained now..." note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 . [1] "maximum number of models that still need to be trained: 5" [1] "model using Sepal.Width,Petal.Width will be trained now..." note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 . [1] "maximum number of models that still need to be trained: 4" [1] "model using Petal.Length,Petal.Width will be trained now..." note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 . [1] "maximum number of models that still need to be trained: 3" [1] "vars selected: Petal.Length,Petal.Width with Accuracy 0.953" [1] "model using additional variable Sepal.Length will be trained now..." note: only 2 unique complexity parameters in default grid. Truncating the grid to 2 . [1] "maximum number of models that still need to be trained: 2" [1] "model using additional variable Sepal.Width will be trained now..." note: only 2 unique complexity parameters in default grid. Truncating the grid to 2 . [1] "maximum number of models that still need to be trained: 1" [1] "vars selected: Petal.Length,Petal.Width,Sepal.Width with Accuracy 0.954" [1] "model using additional variable Sepal.Length will be trained now..." [1] "maximum number of models that still need to be trained: 0" [1] "vars selected: Petal.Length,Petal.Width,Sepal.Width with Accuracy 0.954" [1] "model using Sepal.Length,Sepal.Width will be trained now..." note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 . [1] "maximum number of models that still need to be trained: 8" [1] "model using Sepal.Length,Petal.Length will be trained now..." note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 . [1] "maximum number of models that still need to be trained: 7" [1] "model using Sepal.Length,Petal.Width will be trained now..." note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 . [1] "maximum number of models that still need to be trained: 6" [1] "model using Sepal.Width,Petal.Length will be trained now..." note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 . [1] "maximum number of models that still need to be trained: 5" [1] "model using Sepal.Width,Petal.Width will be trained now..." note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 . [1] "maximum number of models that still need to be trained: 4" [1] "model using Petal.Length,Petal.Width will be trained now..." note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 . [1] "maximum number of models that still need to be trained: 3" [1] "vars selected: Petal.Length,Petal.Width with Accuracy 0.953" [1] "model using additional variable Sepal.Length will be trained now..." note: only 2 unique complexity parameters in default grid. Truncating the grid to 2 . [1] "maximum number of models that still need to be trained: 2" [1] "model using additional variable Sepal.Width will be trained now..." note: only 2 unique complexity parameters in default grid. Truncating the grid to 2 . [1] "maximum number of models that still need to be trained: 1" [1] "vars selected: Petal.Length,Petal.Width,Sepal.Width with Accuracy 0.955" [1] "model using additional variable Sepal.Length will be trained now..." [1] "maximum number of models that still need to be trained: 0" [1] "vars selected: Petal.Length,Petal.Width,Sepal.Width with Accuracy 0.955" [1] "model using Sepal.Length,Sepal.Width will be trained now..." note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 . [1] "maximum number of models that still need to be trained: 8" [1] "model using Sepal.Length,Petal.Length will be trained now..." note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 . [1] "maximum number of models that still need to be trained: 7" [1] "model using Sepal.Length,Petal.Width will be trained now..." note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 . [1] "maximum number of models that still need to be trained: 6" [1] "model using Sepal.Width,Petal.Length will be trained now..." note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 . [1] "maximum number of models that still need to be trained: 5" [1] "model using Sepal.Width,Petal.Width will be trained now..." note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 . [1] "maximum number of models that still need to be trained: 4" [1] "model using Petal.Length,Petal.Width will be trained now..." note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 . [1] "maximum number of models that still need to be trained: 3" [1] "vars selected: Petal.Length,Petal.Width with Accuracy 0.953" [1] "model using additional variable Sepal.Length will be trained now..." note: only 2 unique complexity parameters in default grid. Truncating the grid to 2 . [1] "maximum number of models that still need to be trained: 2" [1] "model using additional variable Sepal.Width will be trained now..." note: only 2 unique complexity parameters in default grid. Truncating the grid to 2 . [1] "maximum number of models that still need to be trained: 1" [1] "vars selected: Petal.Length,Petal.Width with Accuracy 0.953" Note: No increase in performance found using more than 2 variables [1] "model using Sepal.Length,Sepal.Width,Petal.Length,Petal.Width will be trained now..." [1] "maximum number of models that still need to be trained: 0" [1] "vars selected: Sepal.Length,Sepal.Width,Petal.Length,Petal.Width with Accuracy 0.952" [1] "model using additional variable NA will be trained now..." Spherical geometry (s2) switched off Spherical geometry (s2) switched on Spherical geometry (s2) switched off Spherical geometry (s2) switched on features are extracted from the modeldomain samplesize for new data shouldn't be larger than number of pixels. Samplesize was reduced to 100 Spherical geometry (s2) switched off Spherical geometry (s2) switched on features are extracted from the modeldomain samplesize for new data shouldn't be larger than number of pixels. Samplesize was reduced to 100 Spherical geometry (s2) switched off features are extracted from the modeldomain samplesize for new data shouldn't be larger than number of pixels. Samplesize was reduced to 100 Spherical geometry (s2) switched on features are extracted from the modeldomain samplesize for new data shouldn't be larger than number of pixels. Samplesize was reduced to 100 Spherical geometry (s2) switched off variable(s) 'fct' is (are) treated as categorical variables time variable that has been selected: Date time variable that has been selected: Date time variable that has been selected: Date note: only 2 unique complexity parameters in default grid. Truncating the grid to 2 . note: only 2 unique complexity parameters in default grid. Truncating the grid to 2 . Spherical geometry (s2) switched on although coordinates are longitude/latitude, st_sample assumes that they are planar although coordinates are longitude/latitude, st_sample assumes that they are planar 1000 prediction points are sampled from the modeldomain Gij <= Gj; a random CV assignment is returned 1000 prediction points are sampled from the modeldomain Gij <= Gj; a random CV assignment is returned 1000 prediction points are sampled from the modeldomain predictor values are extracted for prediction points Gij <= Gj; a random CV assignment is returned 1000 prediction points are sampled from the modeldomain predictor values are extracted for prediction points Gij <= Gj; a random CV assignment is returned 1000 prediction points are sampled from the modeldomain although coordinates are longitude/latitude, st_sample assumes that they are planar predictor values are extracted for prediction points variable(s) 'fct' is (are) treated as categorical variables some prediction points contain NAs, which will be removed Gij <= Gj; a random CV assignment is returned 1000 prediction points are sampled from the modeldomain predictor values are extracted for prediction points variable(s) 'fct' is (are) treated as categorical variables 1000 prediction points are sampled from the modeldomain predictor values are extracted for prediction points variable(s) 'fct' is (are) treated as categorical variables 1000 prediction points are sampled from the modeldomain although coordinates are longitude/latitude, st_sample assumes that they are planar predictor values are extracted for prediction points 1000 prediction points are sampled from the modeldomain predictor values are extracted for prediction points 1000 prediction points are sampled from the modeldomain predictor values are extracted for prediction points 1000 prediction points are sampled from the modeldomain 1000 prediction points are sampled from the modeldomain 1000 prediction points are sampled from the modeldomain 1000 prediction points are sampled from the modeldomain [ FAIL 0 | WARN 0 | SKIP 0 | PASS 133 ] > > proc.time() user system elapsed 184.42 12.84 197.29