Running 'spelling.R' [1s]
  Running 'testthat.R' [11s]
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:
       ▆
    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$fit, newdata = new_data, dist = "mahalanobis.dist")
   12. └─mixOmics:::predict.mixo_spls(...)
   13.   └─methods::is(object, c("rgcca", "sparse.rgcca"))
   14.     └─base::stopifnot(length(class2) == 1L)
  ── Error (test-class-pls.R:46:3): classification model fitting ─────────────────
  Error in `is(object, c("rgcca", "sparse.rgcca"))`: length(class2) == 1L is not TRUE
  Backtrace:
      ▆
   1. ├─stats::predict(uni_model, x_te) at test-class-pls.R:46:2
   2. └─mixOmics:::predict.mixo_pls(uni_model, x_te)
   3.   └─methods::is(object, c("rgcca", "sparse.rgcca"))
   4.     └─base::stopifnot(length(class2) == 1L)
  ── Failure (test-class-spls.R:42:3): classification model fitting ──────────────
  `parsnip_spls_da_class <- predict(parsnip_spls_da, as.data.frame(x_te))` threw an error.
  Message: length(class2) == 1L is not TRUE
  Class:   simpleError/error/condition
  Backtrace:
       ▆
    1. ├─testthat::expect_error(...) at test-class-spls.R:42:2
    2. │ └─testthat:::quasi_capture(...)
    3. │   ├─testthat .capture(...)
    4. │   │ └─base::withCallingHandlers(...)
    5. │   └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
    6. ├─stats::predict(parsnip_spls_da, as.data.frame(x_te))
    7. ├─parsnip::predict.model_fit(parsnip_spls_da, as.data.frame(x_te))
    8. │ ├─parsnip:::predict_class(...)
    9. │ └─parsnip::predict_class.model_fit(...)
   10. │   └─rlang::eval_tidy(pred_call)
   11. ├─stats::predict(object = object$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:
      ▆
   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:
       ▆
    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$fit, newdata = new_data, dist = "mahalanobis.dist")
   12. └─mixOmics:::predict.mixo_spls(...)
   13.   └─methods::is(object, c("rgcca", "sparse.rgcca"))
   14.     └─base::stopifnot(length(class2) == 1L)
  ── Error (test-reg-pls.R:46:3): Multivariate model fitting ─────────────────────
  Error in `is(object, c("rgcca", "sparse.rgcca"))`: length(class2) == 1L is not TRUE
  Backtrace:
      ▆
   1. ├─stats::predict(multi_model, x_te) at test-reg-pls.R:46:2
   2. └─mixOmics:::predict.mixo_pls(multi_model, x_te)
   3.   └─methods::is(object, c("rgcca", "sparse.rgcca"))
   4.     └─base::stopifnot(length(class2) == 1L)
  ── Failure (test-reg-pls.R:91:3): Univariate model fitting ─────────────────────
  `parsnip_pls_uni_num <- predict(parsnip_pls_uni, as.data.frame(x_te))` threw an error.
  Message: length(class2) == 1L is not TRUE
  Class:   simpleError/error/condition
  Backtrace:
       ▆
    1. ├─testthat::expect_error(...) at test-reg-pls.R:91:2
    2. │ └─testthat:::quasi_capture(...)
    3. │   ├─testthat .capture(...)
    4. │   │ └─base::withCallingHandlers(...)
    5. │   └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
    6. ├─stats::predict(parsnip_pls_uni, as.data.frame(x_te))
    7. ├─parsnip::predict.model_fit(parsnip_pls_uni, as.data.frame(x_te))
    8. │ ├─parsnip::predict_numeric(...)
    9. │ └─parsnip::predict_numeric.model_fit(...)
   10. │   └─rlang::eval_tidy(pred_call)
   11. ├─stats::predict(object = object$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:
      ▆
   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:
       ▆
    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$fit, newdata = new_data, dist = "mahalanobis.dist")
   12. └─mixOmics:::predict.mixo_spls(...)
   13.   └─methods::is(object, c("rgcca", "sparse.rgcca"))
   14.     └─base::stopifnot(length(class2) == 1L)
  ── Failure (test-reg-pls.R:146:3): dummy variable encodings ────────────────────
  `multi_predict(parsnip_pls_multi, penguins, num_comp = 1:3)` threw an error.
  Message: length(class2) == 1L is not TRUE
  Class:   simpleError/error/condition
  Backtrace:
       ▆
    1. ├─testthat::expect_error(...) at test-reg-pls.R:146:2
    2. │ └─testthat:::quasi_capture(...)
    3. │   ├─testthat .capture(...)
    4. │   │ └─base::withCallingHandlers(...)
    5. │   └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
    6. ├─parsnip::multi_predict(parsnip_pls_multi, penguins, num_comp = 1:3)
    7. └─plsmod:::multi_predict._mixo_spls(...)
    8.   └─plsmod:::multi_numeric_preds(object, new_data, num_comp)
    9.     ├─stats::predict(object$fit, new_data, dist = "mahalanobis.dist")
   10.     └─mixOmics:::predict.mixo_spls(object$fit, new_data, dist = "mahalanobis.dist")
   11.       └─methods::is(object, c("rgcca", "sparse.rgcca"))
   12.         └─base::stopifnot(length(class2) == 1L)
  ── Failure (test-reg-spls.R:34:3): Multivariate model fitting ──────────────────
  `parsnip_spls_multi_num <- predict(parsnip_spls_multi, as.data.frame(x_te))` threw an error.
  Message: length(class2) == 1L is not TRUE
  Class:   simpleError/error/condition
  Backtrace:
       ▆
    1. ├─testthat::expect_error(...) at test-reg-spls.R:34:2
    2. │ └─testthat:::quasi_capture(...)
    3. │   ├─testthat .capture(...)
    4. │   │ └─base::withCallingHandlers(...)
    5. │   └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
    6. ├─stats::predict(parsnip_spls_multi, as.data.frame(x_te))
    7. ├─parsnip::predict.model_fit(parsnip_spls_multi, as.data.frame(x_te))
    8. │ ├─parsnip::predict_numeric(...)
    9. │ └─parsnip::predict_numeric.model_fit(...)
   10. │   └─rlang::eval_tidy(pred_call)
   11. ├─stats::predict(object = object$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:
      ▆
   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:
       ▆
    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:
      ▆
   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