Running 'testthat.R' [72s/80s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
  > library(testthat)
  > library(scoringTools)
  > 
  > test_check("scoringTools")
  -- Warning (test-augmentation.R:9:3): augmentation method works with speedglm an
  non-integer #successes in a binomial glm!
  
  -- ERROR (test-chiM.R:18:9): chiM method works with speedglm and glm -----------
  Error: cannot xtfrm 'x'
  Backtrace:
       x
    1. \-scoringTools::chiM_iter(...) test-chiM.R:18:8
    2.   +-base::data.frame(...)
    3.   +-base::sapply(...)
    4.   | \-base::lapply(X = X, FUN = FUN, ...)
    5.   +-base::data.frame(...)
    6.   \-scoringTools:::discretize_cutp(...)
    7.     \-scoringTools:::get_cutp(disc_train_set, cont_train_set)
    8.       +-min_cutoff[order(min_cutoff), ]
    9.       +-base::`[.data.frame`(min_cutoff, order(min_cutoff), )
   10.       \-base::order(min_cutoff)
   11.         \-base::lapply(z, function(x) if (is.object(x)) as.vector(xtfrm(x)) else x)
   12.           \-base:::FUN(X[[i]], ...)
   13.             +-base::as.vector(xtfrm(x))
   14.             +-base::xtfrm(x)
   15.             \-base::xtfrm.default(x)
  
  -- Warning (test-fuzzy-augmentation.R:9:3): fuzzy-augmentation method works with
  non-integer #successes in a binomial glm!
  
  -- ERROR (test-mdlp.R:18:9): mdlp method works with speedglm and glm -----------
  Error: cannot xtfrm 'x'
  Backtrace:
       x
    1. \-scoringTools::mdlp_iter(...) test-mdlp.R:18:8
    2.   +-base::data.frame(...)
    3.   +-base::sapply(...)
    4.   | \-base::lapply(X = X, FUN = FUN, ...)
    5.   +-base::data.frame(...)
    6.   \-scoringTools:::discretize_cutp(...)
    7.     \-scoringTools:::get_cutp(disc_train_set, cont_train_set)
    8.       +-min_cutoff[order(min_cutoff), ]
    9.       +-base::`[.data.frame`(min_cutoff, order(min_cutoff), )
   10.       \-base::order(min_cutoff)
   11.         \-base::lapply(z, function(x) if (is.object(x)) as.vector(xtfrm(x)) else x)
   12.           \-base:::FUN(X[[i]], ...)
   13.             +-base::as.vector(xtfrm(x))
   14.             +-base::xtfrm(x)
   15.             \-base::xtfrm.default(x)
  
  -- Warning (test-methods.R:17:3): show method for reject works -----------------
  non-integer #successes in a binomial glm!
  
  Generalized Linear Model of class 'speedglm':
  
  Call:  speedglm::speedglm(formula = labels ~ ., data = df_augmente[,      -which(names(df_augmente) %in% c("poidsfinal", "classe_SCORE"))][!df_augmente$poidsfinal ==      0, ], family = stats::binomial(link = "logit"), weights = df_augmente$poidsfinal[!df_augmente$poidsfinal ==      0]) 
  
  Coefficients:
  (Intercept)          x.1          x.2  
       0.0102       0.7197      -1.2085  
  
  Generalized Linear Model of class 'speedglm':
  
  Call:  speedglm::speedglm(formula = stats::formula("labels ~ ."), data = Filter(function(x) (length(unique(x)) >      1), data.frame(sapply(disc[[i]]$Disc.data, as.factor), stringsAsFactors = TRUE)),      family = stats::binomial(link = "logit"), weights = NULL,      fitted = TRUE) 
  
  Coefficients:
  (Intercept)          X22          X32  
         1.55        -2.77        -1.93  
  
  Generalized Linear Model of class 'speedglm':
  
  Call:  speedglm::speedglm(formula = stats::formula("labels ~ ."), data = Filter(function(x) (length(unique(x)) >      1), data.frame(sapply(disc[[i]]$Disc.data, as.factor), stringsAsFactors = TRUE)),      family = stats::binomial(link = "logit"), weights = NULL,      fitted = TRUE) 
  
  Coefficients:
  (Intercept)          X22          X32  
         1.55        -2.77        -1.93  
  
  -- ERROR (test-methods.R:51:3): predict method for discretization works --------
  Error: cannot xtfrm 'x'
  Backtrace:
       x
    1. \-scoringTools::chi2_iter(x, y) test-methods.R:51:2
    2.   +-methods::new(...)
    3.   | +-methods::initialize(value, ...)
    4.   | \-methods::initialize(value, ...)
    5.   +-base::data.frame(...)
    6.   \-scoringTools:::discretize_cutp(...)
    7.     \-scoringTools:::get_cutp(disc_train_set, cont_train_set)
    8.       +-min_cutoff[order(min_cutoff), ]
    9.       +-base::`[.data.frame`(min_cutoff, order(min_cutoff), )
   10.       \-base::order(min_cutoff)
   11.         \-base::lapply(z, function(x) if (is.object(x)) as.vector(xtfrm(x)) else x)
   12.           \-base:::FUN(X[[i]], ...)
   13.             +-base::as.vector(xtfrm(x))
   14.             +-base::xtfrm(x)
   15.             \-base::xtfrm.default(x)
  
  -- Warning (test-methods.R:66:3): predict method for reject inference works ----
  non-integer #successes in a binomial glm!
  
  -- Warning (test-methods.R:73:3): predict method for reject inference works ----
  non-integer #successes in a binomial glm!
  
  -- ERROR (test-methods.R:93:3): plot method for discretization works -----------
  Error: cannot xtfrm 'x'
  Backtrace:
       x
    1. \-scoringTools::chi2_iter(x, y) test-methods.R:93:2
    2.   +-methods::new(...)
    3.   | +-methods::initialize(value, ...)
    4.   | \-methods::initialize(value, ...)
    5.   +-base::data.frame(...)
    6.   \-scoringTools:::discretize_cutp(...)
    7.     \-scoringTools:::get_cutp(disc_train_set, cont_train_set)
    8.       +-min_cutoff[order(min_cutoff), ]
    9.       +-base::`[.data.frame`(min_cutoff, order(min_cutoff), )
   10.       \-base::order(min_cutoff)
   11.         \-base::lapply(z, function(x) if (is.object(x)) as.vector(xtfrm(x)) else x)
   12.           \-base:::FUN(X[[i]], ...)
   13.             +-base::as.vector(xtfrm(x))
   14.             +-base::xtfrm(x)
   15.             \-base::xtfrm.default(x)
  
  -- ERROR (test-methods.R:145:3): discretize method for discretization works ----
  Error: cannot xtfrm 'x'
  Backtrace:
       x
    1. \-scoringTools::chi2_iter(x, y) test-methods.R:145:2
    2.   +-methods::new(...)
    3.   | +-methods::initialize(value, ...)
    4.   | \-methods::initialize(value, ...)
    5.   +-base::data.frame(...)
    6.   \-scoringTools:::discretize_cutp(...)
    7.     \-scoringTools:::get_cutp(disc_train_set, cont_train_set)
    8.       +-min_cutoff[order(min_cutoff), ]
    9.       +-base::`[.data.frame`(min_cutoff, order(min_cutoff), )
   10.       \-base::order(min_cutoff)
   11.         \-base::lapply(z, function(x) if (is.object(x)) as.vector(xtfrm(x)) else x)
   12.           \-base:::FUN(X[[i]], ...)
   13.             +-base::as.vector(xtfrm(x))
   14.             +-base::xtfrm(x)
   15.             \-base::xtfrm.default(x)
  
  -- ERROR (test-topdown.R:18:9): topdown method works with speedglm -------------
  Error: cannot xtfrm 'x'
  Backtrace:
       x
    1. \-scoringTools::topdown_iter(...) test-topdown.R:18:8
    2.   +-base::data.frame(...)
    3.   +-base::sapply(...)
    4.   | \-base::lapply(X = X, FUN = FUN, ...)
    5.   +-base::data.frame(...)
    6.   \-scoringTools:::discretize_cutp(...)
    7.     \-scoringTools:::get_cutp(disc_train_set, cont_train_set)
    8.       +-min_cutoff[order(min_cutoff), ]
    9.       +-base::`[.data.frame`(min_cutoff, order(min_cutoff), )
   10.       \-base::order(min_cutoff)
   11.         \-base::lapply(z, function(x) if (is.object(x)) as.vector(xtfrm(x)) else x)
   12.           \-base:::FUN(X[[i]], ...)
   13.             +-base::as.vector(xtfrm(x))
   14.             +-base::xtfrm(x)
   15.             \-base::xtfrm.default(x)
  
  == testthat results  ===========================================================
  Warning (test-augmentation.R:9:3): augmentation method works with speedglm and glm
  ERROR (test-chiM.R:18:9): chiM method works with speedglm and glm
  Warning (test-fuzzy-augmentation.R:9:3): fuzzy-augmentation method works with speedglm and glm
  ERROR (test-mdlp.R:18:9): mdlp method works with speedglm and glm
  Warning (test-methods.R:17:3): show method for reject works
  ERROR (test-methods.R:51:3): predict method for discretization works
  Warning (test-methods.R:66:3): predict method for reject inference works
  Warning (test-methods.R:73:3): predict method for reject inference works
  ERROR (test-methods.R:93:3): plot method for discretization works
  ERROR (test-methods.R:145:3): discretize method for discretization works
  ERROR (test-topdown.R:18:9): topdown method works with speedglm
  
  [ FAIL 6 | WARN 5 | SKIP 0 | PASS 343 ]
  Error: Test failures
  Execution halted