Running 'spelling.R' [0s/1s]
  Running 'testthat.R' [12s/16s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
  > library(testthat)
  > library(plsmod)
  Loading required package: parsnip
  > 
  > test_check("plsmod")
  keepX is of length 4 while ncomp is 3 
  trimming keepX to [33mc(4,4,4)[39mkeepX is of length 4 while ncomp is 3 
  trimming keepX to [33mc(2,2,2)[39mkeepX is of length 4 while ncomp is 3 
  trimming keepX to [33mc(2,2,2)[39mkeepX is of length 100 while ncomp is 3 
  trimming keepX to [33mc(100,100,100)[39mkeepX is of length 100 while ncomp is 3 
  trimming keepX to [33mc(100,100,100)[39mkeepX is of length 8 while ncomp is 3 
  trimming keepX to [33mc(8,8,8)[39mkeepX is of length 100 while ncomp is 3 
  trimming keepX to [33mc(100,100,100)[39mkeepX is of length 100 while ncomp is 3 
  trimming keepX to [33mc(100,100,100)[39mkeepX is of length 100 while ncomp is 3 
  trimming keepX to [33mc(100,100,100)[39mkeepX is of length 100 while ncomp is 3 
  trimming keepX to [33mc(100,100,100)[39m[ FAIL 14 | WARN 0 | SKIP 0 | PASS 14 ]
  
  == Failed tests ================================================================
  -- Failure (test-class-pls.R:41:3): classification model fitting ---------------
  `parsnip_pls_da_class <- predict(parsnip_pls_da, as.data.frame(x_te))` threw an error.
  Message: length(class2) == 1L is not TRUE
  Class:   simpleError/error/condition
  Backtrace:
       x
    1. +-testthat::expect_error(...) at test-class-pls.R:41:2
    2. | \-testthat:::quasi_capture(...)
    3. |   +-testthat .capture(...)
    4. |   | \-base::withCallingHandlers(...)
    5. |   \-rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
    6. +-stats::predict(parsnip_pls_da, as.data.frame(x_te))
    7. +-parsnip::predict.model_fit(parsnip_pls_da, as.data.frame(x_te))
    8. | +-parsnip:::predict_class(...)
    9. | \-parsnip::predict_class.model_fit(...)
   10. |   \-rlang::eval_tidy(pred_call)
   11. +-stats::predict(object = object$fit, newdata = new_data, dist = "mahalanobis.dist")
   12. \-mixOmics:::predict.mixo_spls(...)
   13.   \-methods::is(object, c("rgcca", "sparse.rgcca"))
   14.     \-base::stopifnot(length(class2) == 1L)
  -- Error (test-class-pls.R:46:3): classification model fitting -----------------
  Error in `is(object, c("rgcca", "sparse.rgcca"))`: length(class2) == 1L is not TRUE
  Backtrace:
      x
   1. +-stats::predict(uni_model, x_te) at test-class-pls.R:46:2
   2. \-mixOmics:::predict.mixo_pls(uni_model, x_te)
   3.   \-methods::is(object, c("rgcca", "sparse.rgcca"))
   4.     \-base::stopifnot(length(class2) == 1L)
  -- Failure (test-class-spls.R:42:3): classification model fitting --------------
  `parsnip_spls_da_class <- predict(parsnip_spls_da, as.data.frame(x_te))` threw an error.
  Message: length(class2) == 1L is not TRUE
  Class:   simpleError/error/condition
  Backtrace:
       x
    1. +-testthat::expect_error(...) at test-class-spls.R:42:2
    2. | \-testthat:::quasi_capture(...)
    3. |   +-testthat .capture(...)
    4. |   | \-base::withCallingHandlers(...)
    5. |   \-rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
    6. +-stats::predict(parsnip_spls_da, as.data.frame(x_te))
    7. +-parsnip::predict.model_fit(parsnip_spls_da, as.data.frame(x_te))
    8. | +-parsnip:::predict_class(...)
    9. | \-parsnip::predict_class.model_fit(...)
   10. |   \-rlang::eval_tidy(pred_call)
   11. +-stats::predict(object = object$fit, newdata = new_data, dist = "mahalanobis.dist")
   12. \-mixOmics:::predict.mixo_spls(...)
   13.   \-methods::is(object, c("rgcca", "sparse.rgcca"))
   14.     \-base::stopifnot(length(class2) == 1L)
  -- Error (test-class-spls.R:47:3): classification model fitting ----------------
  Error in `is(object, c("rgcca", "sparse.rgcca"))`: length(class2) == 1L is not TRUE
  Backtrace:
      x
   1. +-stats::predict(uni_model, x_te) at test-class-spls.R:47:2
   2. \-mixOmics:::predict.mixo_spls(uni_model, x_te)
   3.   \-methods::is(object, c("rgcca", "sparse.rgcca"))
   4.     \-base::stopifnot(length(class2) == 1L)
  -- Failure (test-reg-pls.R:41:3): Multivariate model fitting -------------------
  `parsnip_pls_multi_num <- predict(parsnip_pls_multi, as.data.frame(x_te))` threw an error.
  Message: length(class2) == 1L is not TRUE
  Class:   simpleError/error/condition
  Backtrace:
       x
    1. +-testthat::expect_error(...) at test-reg-pls.R:41:2
    2. | \-testthat:::quasi_capture(...)
    3. |   +-testthat .capture(...)
    4. |   | \-base::withCallingHandlers(...)
    5. |   \-rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
    6. +-stats::predict(parsnip_pls_multi, as.data.frame(x_te))
    7. +-parsnip::predict.model_fit(parsnip_pls_multi, as.data.frame(x_te))
    8. | +-parsnip::predict_numeric(...)
    9. | \-parsnip::predict_numeric.model_fit(...)
   10. |   \-rlang::eval_tidy(pred_call)
   11. +-stats::predict(object = object$fit, newdata = new_data, dist = "mahalanobis.dist")
   12. \-mixOmics:::predict.mixo_spls(...)
   13.   \-methods::is(object, c("rgcca", "sparse.rgcca"))
   14.     \-base::stopifnot(length(class2) == 1L)
  -- Error (test-reg-pls.R:46:3): Multivariate model fitting ---------------------
  Error in `is(object, c("rgcca", "sparse.rgcca"))`: length(class2) == 1L is not TRUE
  Backtrace:
      x
   1. +-stats::predict(multi_model, x_te) at test-reg-pls.R:46:2
   2. \-mixOmics:::predict.mixo_pls(multi_model, x_te)
   3.   \-methods::is(object, c("rgcca", "sparse.rgcca"))
   4.     \-base::stopifnot(length(class2) == 1L)
  -- Failure (test-reg-pls.R:91:3): Univariate model fitting ---------------------
  `parsnip_pls_uni_num <- predict(parsnip_pls_uni, as.data.frame(x_te))` threw an error.
  Message: length(class2) == 1L is not TRUE
  Class:   simpleError/error/condition
  Backtrace:
       x
    1. +-testthat::expect_error(...) at test-reg-pls.R:91:2
    2. | \-testthat:::quasi_capture(...)
    3. |   +-testthat .capture(...)
    4. |   | \-base::withCallingHandlers(...)
    5. |   \-rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
    6. +-stats::predict(parsnip_pls_uni, as.data.frame(x_te))
    7. +-parsnip::predict.model_fit(parsnip_pls_uni, as.data.frame(x_te))
    8. | +-parsnip::predict_numeric(...)
    9. | \-parsnip::predict_numeric.model_fit(...)
   10. |   \-rlang::eval_tidy(pred_call)
   11. +-stats::predict(object = object$fit, newdata = new_data, dist = "mahalanobis.dist")
   12. \-mixOmics:::predict.mixo_spls(...)
   13.   \-methods::is(object, c("rgcca", "sparse.rgcca"))
   14.     \-base::stopifnot(length(class2) == 1L)
  -- Error (test-reg-pls.R:96:3): Univariate model fitting -----------------------
  Error in `is(object, c("rgcca", "sparse.rgcca"))`: length(class2) == 1L is not TRUE
  Backtrace:
      x
   1. +-stats::predict(uni_model, x_te) at test-reg-pls.R:96:2
   2. \-mixOmics:::predict.mixo_pls(uni_model, x_te)
   3.   \-methods::is(object, c("rgcca", "sparse.rgcca"))
   4.     \-base::stopifnot(length(class2) == 1L)
  -- Failure (test-reg-pls.R:142:3): dummy variable encodings --------------------
  `predict(parsnip_pls_multi, penguins)` threw an error.
  Message: length(class2) == 1L is not TRUE
  Class:   simpleError/error/condition
  Backtrace:
       x
    1. +-testthat::expect_error(...) at test-reg-pls.R:142:2
    2. | \-testthat:::quasi_capture(...)
    3. |   +-testthat .capture(...)
    4. |   | \-base::withCallingHandlers(...)
    5. |   \-rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
    6. +-stats::predict(parsnip_pls_multi, penguins)
    7. +-parsnip::predict.model_fit(parsnip_pls_multi, penguins)
    8. | +-parsnip::predict_numeric(...)
    9. | \-parsnip::predict_numeric.model_fit(...)
   10. |   \-rlang::eval_tidy(pred_call)
   11. +-stats::predict(object = object$fit, newdata = new_data, dist = "mahalanobis.dist")
   12. \-mixOmics:::predict.mixo_spls(...)
   13.   \-methods::is(object, c("rgcca", "sparse.rgcca"))
   14.     \-base::stopifnot(length(class2) == 1L)
  -- Failure (test-reg-pls.R:146:3): dummy variable encodings --------------------
  `multi_predict(parsnip_pls_multi, penguins, num_comp = 1:3)` threw an error.
  Message: length(class2) == 1L is not TRUE
  Class:   simpleError/error/condition
  Backtrace:
       x
    1. +-testthat::expect_error(...) at test-reg-pls.R:146:2
    2. | \-testthat:::quasi_capture(...)
    3. |   +-testthat .capture(...)
    4. |   | \-base::withCallingHandlers(...)
    5. |   \-rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
    6. +-parsnip::multi_predict(parsnip_pls_multi, penguins, num_comp = 1:3)
    7. \-plsmod:::multi_predict._mixo_spls(...)
    8.   \-plsmod:::multi_numeric_preds(object, new_data, num_comp)
    9.     +-stats::predict(object$fit, new_data, dist = "mahalanobis.dist")
   10.     \-mixOmics:::predict.mixo_spls(object$fit, new_data, dist = "mahalanobis.dist")
   11.       \-methods::is(object, c("rgcca", "sparse.rgcca"))
   12.         \-base::stopifnot(length(class2) == 1L)
  -- Failure (test-reg-spls.R:34:3): Multivariate model fitting ------------------
  `parsnip_spls_multi_num <- predict(parsnip_spls_multi, as.data.frame(x_te))` threw an error.
  Message: length(class2) == 1L is not TRUE
  Class:   simpleError/error/condition
  Backtrace:
       x
    1. +-testthat::expect_error(...) at test-reg-spls.R:34:2
    2. | \-testthat:::quasi_capture(...)
    3. |   +-testthat .capture(...)
    4. |   | \-base::withCallingHandlers(...)
    5. |   \-rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
    6. +-stats::predict(parsnip_spls_multi, as.data.frame(x_te))
    7. +-parsnip::predict.model_fit(parsnip_spls_multi, as.data.frame(x_te))
    8. | +-parsnip::predict_numeric(...)
    9. | \-parsnip::predict_numeric.model_fit(...)
   10. |   \-rlang::eval_tidy(pred_call)
   11. +-stats::predict(object = object$fit, newdata = new_data, dist = "mahalanobis.dist")
   12. \-mixOmics:::predict.mixo_spls(...)
   13.   \-methods::is(object, c("rgcca", "sparse.rgcca"))
   14.     \-base::stopifnot(length(class2) == 1L)
  -- Error (test-reg-spls.R:39:3): Multivariate model fitting --------------------
  Error in `is(object, c("rgcca", "sparse.rgcca"))`: length(class2) == 1L is not TRUE
  Backtrace:
      x
   1. +-stats::predict(multi_model, x_te) at test-reg-spls.R:39:2
   2. \-mixOmics:::predict.mixo_spls(multi_model, x_te)
   3.   \-methods::is(object, c("rgcca", "sparse.rgcca"))
   4.     \-base::stopifnot(length(class2) == 1L)
  -- Failure (test-reg-spls.R:84:3): Univariate model fitting --------------------
  `parsnip_spls_uni_num <- predict(parsnip_spls_uni, as.data.frame(x_te))` threw an error.
  Message: length(class2) == 1L is not TRUE
  Class:   simpleError/error/condition
  Backtrace:
       x
    1. +-testthat::expect_error(...) at test-reg-spls.R:84:2
    2. | \-testthat:::quasi_capture(...)
    3. |   +-testthat .capture(...)
    4. |   | \-base::withCallingHandlers(...)
    5. |   \-rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
    6. +-stats::predict(parsnip_spls_uni, as.data.frame(x_te))
    7. +-parsnip::predict.model_fit(parsnip_spls_uni, as.data.frame(x_te))
    8. | +-parsnip::predict_numeric(...)
    9. | \-parsnip::predict_numeric.model_fit(...)
   10. |   \-rlang::eval_tidy(pred_call)
   11. +-stats::predict(object = object$fit, newdata = new_data, dist = "mahalanobis.dist")
   12. \-mixOmics:::predict.mixo_spls(...)
   13.   \-methods::is(object, c("rgcca", "sparse.rgcca"))
   14.     \-base::stopifnot(length(class2) == 1L)
  -- Error (test-reg-spls.R:89:3): Univariate model fitting ----------------------
  Error in `is(object, c("rgcca", "sparse.rgcca"))`: length(class2) == 1L is not TRUE
  Backtrace:
      x
   1. +-stats::predict(uni_model, x_te) at test-reg-spls.R:89:2
   2. \-mixOmics:::predict.mixo_spls(uni_model, x_te)
   3.   \-methods::is(object, c("rgcca", "sparse.rgcca"))
   4.     \-base::stopifnot(length(class2) == 1L)
  
  [ FAIL 14 | WARN 0 | SKIP 0 | PASS 14 ]
  Error: Test failures
  Execution halted