Running 'testthat.R' [180s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
  > 
  > library(testthat)
  > test_check("strip")
  Loading required package: strip
  
  Call:
  stats::lm(formula = mpg ~ ., data = mtcars)
  
  Coefficients:
  (Intercept)          cyl         disp           hp         drat           wt  
     12.30337     -0.11144      0.01334     -0.02148      0.78711     -3.71530  
         qsec           vs           am         gear         carb  
      0.82104      0.31776      2.52023      0.65541     -0.19942  
  
  
  Call:
  stats::lm(formula = mpg ~ ., data = mtcars)
  
  Coefficients:
  (Intercept)          cyl         disp           hp         drat           wt  
     12.30337     -0.11144      0.01334     -0.02148      0.78711     -3.71530  
         qsec           vs           am         gear         carb  
      0.82104      0.31776      2.52023      0.65541     -0.19942  
  
  
  Call:  stats::glm(formula = SF ~ sex * ldose, family = binomial)
  
  Coefficients:
  (Intercept)         sexM        ldose   sexM:ldose  
      -2.9935       0.1750       0.9060       0.3529  
  
  Degrees of Freedom: 11 Total (i.e. Null);  8 Residual
  Null Deviance:	    124.9 
  Residual Deviance: 4.994 	AIC: 43.1
  
  Call:  stats::glm(formula = SF ~ sex * ldose, family = binomial)
  
  Coefficients:
  (Intercept)         sexM        ldose   sexM:ldose  
      -2.9935       0.1750       0.9060       0.3529  
  
  Degrees of Freedom: 11 Total (i.e. Null);  8 Residual
  Null Deviance:	    124.9 
  Residual Deviance: 4.994 	AIC: 43.1
  Call:
  stats::loess(formula = dist ~ speed, data = cars)
  
  Number of Observations: 50 
  Equivalent Number of Parameters: 4.78 
  Residual Standard Error: 15.29 
  Call:
  stats::loess(formula = dist ~ speed, data = cars)
  
  Number of Observations: 50 
  Equivalent Number of Parameters: 4.78 
  Residual Standard Error: 15.29 
  
  Call:
   randomForest(formula = Species ~ ., data = iris) 
                 Type of random forest: classification
                       Number of trees: 500
  No. of variables tried at each split: 2
  
          OOB estimate of  error rate: 4.67%
  Confusion matrix:
             setosa versicolor virginica class.error
  setosa         50          0         0        0.00
  versicolor      0         47         3        0.06
  virginica       0          4        46        0.08
  
  Call:
   randomForest(formula = Species ~ ., data = iris) 
                 Type of random forest: classification
                       Number of trees: 500
  No. of variables tried at each split: 2
  
          OOB estimate of  error rate: 4.67%
  Confusion matrix:
             setosa versicolor virginica class.error
  setosa         50          0         0        0.00
  versicolor      0         47         3        0.06
  virginica       0          4        46        0.08
  Random Forest 
  
  150 samples
    4 predictor
    3 classes: 'setosa', 'versicolor', 'virginica' 
  
  No pre-processing
  Resampling: Bootstrapped (25 reps) 
  Summary of sample sizes: 150, 150, 150, 150, 150, 150, ... 
  Resampling results across tuning parameters:
  
    mtry  Accuracy   Kappa    
    2     0.9413743  0.9109756
    3     0.9429388  0.9133265
    4     0.9394361  0.9080807
  
  Accuracy was used to select the optimal model using the largest value.
  The final value used for the model was mtry = 3.
  Random Forest 
  
  150 samples
    4 predictor
    3 classes: 'setosa', 'versicolor', 'virginica' 
  
  No pre-processing
  Resampling: Bootstrapped (25 reps) 
  Summary of sample sizes: 150, 150, 150, 150, 150, 150, ... 
  Resampling results across tuning parameters:
  
    mtry  Accuracy   Kappa    
    2     0.9413743  0.9109756
    3     0.9429388  0.9133265
    4     0.9394361  0.9080807
  
  Accuracy was used to select the optimal model using the largest value.
  The final value used for the model was mtry = 3.
  [ FAIL 0 | WARN 0 | SKIP 0 | PASS 20 ]
  > 
  > proc.time()
     user  system elapsed 
     8.40    1.43    9.82