Running ‘testthat.R’ [42s/64s]
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 (???): chi2 method works with speedglm and glm ────────────────────────
  Error: cannot xtfrm 'x'
  Backtrace:
       █
    1. ├─testthat::expect_warning(...)
    2. │ └─testthat:::quasi_capture(enquo(object), label, capture_warnings)
    3. │   ├─testthat:::.capture(...)
    4. │   │ └─base::withCallingHandlers(...)
    5. │   └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
    6. └─scoringTools::chi2_iter(...)
    7.   ├─methods::new(...)
    8.   │ ├─methods::initialize(value, ...)
    9.   │ └─methods::initialize(value, ...)
   10.   ├─base::data.frame(...)
   11.   └─scoringTools:::discretize_cutp(...)
   12.     └─scoringTools:::get_cutp(disc_train_set, cont_train_set)
   13.       ├─min_cutoff[order(min_cutoff), ]
   14.       ├─base::`[.data.frame`(min_cutoff, order(min_cutoff), )
   15.       └─base::order(min_cutoff)
   16.         └─base::lapply(z, function(x) if (is.object(x)) as.vector(xtfrm(x)) else x)
   17.           └─base:::FUN(X[[i]], ...)
   18.             ├─base::as.vector(xtfrm(x))
   19.             ├─base::xtfrm(x)
   20.             └─base::xtfrm.default(x)
  
  ── ERROR (test-chiM.R:18:9): chiM method works with speedglm and glm ───────────
  Error: cannot xtfrm 'x'
  Backtrace:
       █
    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)
  
  ── ERROR (???): echi2 method works with speedglm and glm ───────────────────────
  Error: cannot xtfrm 'x'
  Backtrace:
       █
    1. ├─testthat::expect_warning(...)
    2. │ └─testthat:::quasi_capture(enquo(object), label, capture_warnings)
    3. │   ├─testthat:::.capture(...)
    4. │   │ └─base::withCallingHandlers(...)
    5. │   └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
    6. └─scoringTools::echi2_iter(...)
    7.   ├─methods::new(...)
    8.   │ ├─methods::initialize(value, ...)
    9.   │ └─methods::initialize(value, ...)
   10.   ├─base::data.frame(...)
   11.   └─scoringTools:::discretize_cutp(...)
   12.     └─scoringTools:::get_cutp(disc_train_set, cont_train_set)
   13.       ├─min_cutoff[order(min_cutoff), ]
   14.       ├─base::`[.data.frame`(min_cutoff, order(min_cutoff), )
   15.       └─base::order(min_cutoff)
   16.         └─base::lapply(z, function(x) if (is.object(x)) as.vector(xtfrm(x)) else x)
   17.           └─base:::FUN(X[[i]], ...)
   18.             ├─base::as.vector(xtfrm(x))
   19.             ├─base::xtfrm(x)
   20.             └─base::xtfrm.default(x)
  
  ── Warning (test-fuzzy-augmentation.R:9:3): fuzzy-augmentation method works with
  non-integer #successes in a binomial glm!
  
  ── ERROR (???): mdlp method works with speedglm and glm ────────────────────────
  Error: cannot xtfrm 'x'
  Backtrace:
       █
    1. ├─testthat::expect_warning(...)
    2. │ └─testthat:::quasi_capture(enquo(object), label, capture_warnings)
    3. │   ├─testthat:::.capture(...)
    4. │   │ └─base::withCallingHandlers(...)
    5. │   └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
    6. └─scoringTools::mdlp_iter(...)
    7.   ├─base::data.frame(...)
    8.   ├─base::sapply(...)
    9.   │ └─base::lapply(X = X, FUN = FUN, ...)
   10.   ├─base::data.frame(...)
   11.   └─scoringTools:::discretize_cutp(...)
   12.     └─scoringTools:::get_cutp(disc_train_set, cont_train_set)
   13.       ├─min_cutoff[order(min_cutoff), ]
   14.       ├─base::`[.data.frame`(min_cutoff, order(min_cutoff), )
   15.       └─base::order(min_cutoff)
   16.         └─base::lapply(z, function(x) if (is.object(x)) as.vector(xtfrm(x)) else x)
   17.           └─base:::FUN(X[[i]], ...)
   18.             ├─base::as.vector(xtfrm(x))
   19.             ├─base::xtfrm(x)
   20.             └─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  
        0.176        2.179       -2.574  
  
  ── ERROR (test-methods.R:33:3): show method for discretization works ───────────
  Error: cannot xtfrm 'x'
  Backtrace:
       █
    1. └─scoringTools::chi2_iter(x, y) test-methods.R:33:2
    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!
  
  ── Warning (???): plot method for discretization works ─────────────────────────
  Package 'plotly' suggested but not installed, falling back to 'graphics' package.
  
  ── Warning (???): plot method for discretization works ─────────────────────────
  Package 'plotly' suggested but not installed, falling back to 'graphics' package.
  
  ── Warning (???): plot method for discretization works ─────────────────────────
  Package 'plotly' suggested but not installed, falling back to 'graphics' package.
  
  ── Warning (???): plot method for discretization works ─────────────────────────
  Speedglm not installed, using glm instead (slower).
  
  ── Warning (???): plot method for discretization works ─────────────────────────
  Package 'plotly' suggested but not installed, falling back to 'graphics' package.
  
  ── ERROR (test-topdown.R:18:9): topdown method works with speedglm ─────────────
  Error: cannot xtfrm 'x'
  Backtrace:
       █
    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 (???): chi2 method works with speedglm and glm
  ERROR (test-chiM.R:18:9): chiM method works with speedglm and glm
  ERROR (???): echi2 method works with speedglm and glm
  Warning (test-fuzzy-augmentation.R:9:3): fuzzy-augmentation method works with speedglm and glm
  ERROR (???): mdlp method works with speedglm and glm
  Warning (test-methods.R:17:3): show method for reject works
  ERROR (test-methods.R:33:3): show 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
  Warning (???): plot method for discretization works
  Warning (???): plot method for discretization works
  Warning (???): plot method for discretization works
  Warning (???): plot method for discretization works
  Warning (???): plot method for discretization works
  ERROR (test-topdown.R:18:9): topdown method works with speedglm
  
  [ FAIL 6 | WARN 10 | SKIP 0 | PASS 262 ]
  Error: Test failures
  Execution halted