R Under development (unstable) (2023-11-30 r85651 ucrt) -- "Unsuffered Consequences"
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Platform: x86_64-w64-mingw32/x64

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> library(testthat)
> library(iBreakDown)

Attaching package: 'iBreakDown'

The following object is masked from 'package:testthat':

    describe

> 
> test_check("iBreakDown")
Preparation of a new explainer is initiated
  -> model label       :  randomForest  (  default  )
  -> data              :  1000  rows  5  cols 
  -> target variable   :  1000  values 
  -> predict function  :  yhat.randomForest  will be used (  default  )
  -> predicted values  :  No value for predict function target column. (  default  )
  -> model_info        :  package randomForest , ver. 4.7.1.1 , task multiclass (  default  ) 
  -> predicted values  :  predict function returns multiple columns:  3  (  default  ) 
  -> residual function :  difference between 1 and probability of true class (  default  )
  -> residuals         :  numerical, min =  0 , mean =  0.19463 , max =  0.76  
  A new explainer has been created!  
Preparation of a new explainer is initiated
  -> model label       :  randomForest  (  default  )
  -> data              :  1000  rows  5  cols 
  -> target variable   :  1000  values 
  -> predict function  :  yhat.randomForest  will be used (  default  )
  -> predicted values  :  No value for predict function target column. (  default  )
  -> model_info        :  package randomForest , ver. 4.7.1.1 , task multiclass (  default  ) 
  -> predicted values  :  predict function returns multiple columns:  3  (  default  ) 
  -> residual function :  difference between 1 and probability of true class (  default  )
  -> residuals         :  numerical, min =  0 , mean =  0.19463 , max =  0.76  
  A new explainer has been created!  
Preparation of a new explainer is initiated
  -> model label       :  randomForest  (  default  )
  -> data              :  1000  rows  5  cols 
  -> target variable   :  1000  values 
  -> predict function  :  yhat.randomForest  will be used (  default  )
  -> predicted values  :  No value for predict function target column. (  default  )
  -> model_info        :  package randomForest , ver. 4.7.1.1 , task regression (  default  ) 
  -> predicted values  :  numerical, min =  2052.033 , mean =  3487.71 , max =  5776.623  
  -> residual function :  difference between y and yhat (  default  )
  -> residuals         :  numerical, min =  -632.8469 , mean =  1.070017 , max =  1328.352  
  A new explainer has been created!  
Preparation of a new explainer is initiated
  -> model label       :  randomForest  (  default  )
  -> data              :  1000  rows  5  cols 
  -> target variable   :  1000  values 
  -> predict function  :  yhat.randomForest  will be used (  default  )
  -> predicted values  :  No value for predict function target column. (  default  )
  -> model_info        :  package randomForest , ver. 4.7.1.1 , task multiclass (  default  ) 
  -> predicted values  :  predict function returns multiple columns:  3  (  default  ) 
  -> residual function :  difference between 1 and probability of true class (  default  )
  -> residuals         :  numerical, min =  0 , mean =  0.19463 , max =  0.76  
  A new explainer has been created!  
Preparation of a new explainer is initiated
  -> model label       :  randomForest  (  default  )
  -> data              :  1000  rows  5  cols 
  -> target variable   :  1000  values 
  -> predict function  :  yhat.randomForest  will be used (  default  )
  -> predicted values  :  No value for predict function target column. (  default  )
  -> model_info        :  package randomForest , ver. 4.7.1.1 , task regression (  default  ) 
  -> predicted values  :  numerical, min =  2043.066 , mean =  3487.722 , max =  5773.976  
  -> residual function :  difference between y and yhat (  default  )
  -> residuals         :  numerical, min =  -630.6766 , mean =  1.057813 , max =  1256.239  
  A new explainer has been created!  
Preparation of a new explainer is initiated
  -> model label       :  Random Forest v7 
  -> data              :  2099  rows  8  cols 
  -> target variable   :  2099  values 
  -> predict function  :  yhat.randomForest  will be used (  default  )
  -> predicted values  :  No value for predict function target column. (  default  )
  -> model_info        :  package randomForest , ver. 4.7.1.1 , task regression (  default  ) 
  -> model_info        :  Model info detected regression task but 'y' is a logical .  (  WARNING  )
  -> model_info        :  By deafult regressions tasks supports only numercical 'y' parameter. 
  -> model_info        :  Consider changing to numerical vector.
  -> model_info        :  Otherwise I will not be able to calculate residuals or loss function.
  -> predicted values  :  numerical, min =  0.008871262 , mean =  0.3245211 , max =  0.993945  
  -> residual function :  difference between y and yhat (  default  )
  -> residuals         :  numerical, min =  -0.7991837 , mean =  -8.085586e-05 , max =  0.9005136  
  A new explainer has been created!  
Preparation of a new explainer is initiated
  -> model label       :  lm  (  default  )
  -> data              :  500  rows  2  cols 
  -> target variable   :  500  values 
  -> predict function  :  yhat.glm  will be used (  default  )
  -> predicted values  :  No value for predict function target column. (  default  )
  -> model_info        :  package stats , ver. 4.4.0 , task classification (  default  ) 
  -> model_info        :  Model info detected classification task but 'y' is a logical . Converted to numeric.  (  NOTE  )
  -> predicted values  :  numerical, min =  0.1588418 , mean =  0.348 , max =  0.8211798  
  -> residual function :  difference between y and yhat (  default  )
  -> residuals         :  numerical, min =  -0.8192672 , mean =  -1.465488e-13 , max =  0.8145874  
  A new explainer has been created!  
Preparation of a new explainer is initiated
  -> model label       :  randomForest  (  default  )
  -> data              :  1000  rows  5  cols 
  -> target variable   :  1000  values 
  -> predict function  :  yhat.randomForest  will be used (  default  )
  -> predicted values  :  No value for predict function target column. (  default  )
  -> model_info        :  package randomForest , ver. 4.7.1.1 , task multiclass (  default  ) 
  -> predicted values  :  predict function returns multiple columns:  3  (  default  ) 
  -> residual function :  difference between 1 and probability of true class (  default  )
  -> residuals         :  numerical, min =  0 , mean =  0.19463 , max =  0.76  
  A new explainer has been created!  
Preparation of a new explainer is initiated
  -> model label       :  randomForest  (  default  )
  -> data              :  1000  rows  5  cols 
  -> target variable   :  1000  values 
  -> predict function  :  yhat.randomForest  will be used (  default  )
  -> predicted values  :  No value for predict function target column. (  default  )
  -> model_info        :  package randomForest , ver. 4.7.1.1 , task regression (  default  ) 
  -> predicted values  :  numerical, min =  2072.837 , mean =  3487.447 , max =  5770.068  
  -> residual function :  difference between y and yhat (  default  )
  -> residuals         :  numerical, min =  -640.4328 , mean =  1.332642 , max =  1233.249  
  A new explainer has been created!  
Preparation of a new explainer is initiated
  -> model label       :  randomForest  (  default  )
  -> data              :  150  rows  5  cols 
  -> target variable   :  150  values 
  -> predict function  :  yhat.randomForest  will be used (  default  )
  -> predicted values  :  No value for predict function target column. (  default  )
  -> model_info        :  package randomForest , ver. 4.7.1.1 , task regression (  default  ) 
  -> predicted values  :  numerical, min =  1.33308 , mean =  3.752537 , max =  6.228252  
  -> residual function :  difference between y and yhat (  default  )
  -> residuals         :  numerical, min =  -0.5970904 , mean =  0.005462883 , max =  0.6717483  
  A new explainer has been created!  
                                 contribution
randomForest: intercept                 3.753
randomForest: Petal.Width = 0.2        -1.026
randomForest: Species = setosa         -0.863
randomForest: Sepal.Length = 5.1       -0.335
randomForest: Sepal.Width = 3.5        -0.065
randomForest: Petal.Length = 1.4        0.000
randomForest: prediction                1.464
[ FAIL 0 | WARN 2 | SKIP 0 | PASS 67 ]

[ FAIL 0 | WARN 2 | SKIP 0 | PASS 67 ]
> 
> proc.time()
   user  system elapsed 
 100.54   20.06  120.60