Running ‘testthat.R’ [34s/52s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > # This file is part of the standard setup for testthat.
  > # It is recommended that you do not modify it.
  > #
  > # Where should you do additional test configuration?
  > # Learn more about the roles of various files in:
  > # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
  > # * https://testthat.r-lib.org/articles/special-files.html
  > 
  > library(testthat)
  > library(slideimp)
  > 
  > test_check("slideimp")
  Imputing 2 groups using KNN.
  Running mode: sequential
  Imputing 2 groups using KNN.
  Running mode: sequential
  Imputing 2 groups using PCA.
  Running mode: sequential
  Imputing 2 groups using PCA.
  Running mode: sequential
  Imputing 2 groups using KNN.
  Running mode: sequential
  Imputing 2 groups using KNN.
  Running mode: sequential
  Imputing 3 groups using KNN.
  Running mode: sequential
  Imputing 2 groups using KNN.
  Running mode: sequential
  Imputing 2 groups using KNN.
  Running mode: sequential
  Imputing 2 groups using PCA.
  Running mode: sequential
  Imputing 2 groups using KNN.
  Running mode: sequential
  ! 10 columns in `obj` have no matching entry in `group` and will be left untouched.
  i feature1, feature2, feature3, feature4, feature5, ...
  Imputing 2 groups using KNN.
  Running mode: sequential
  Imputing 2 groups using PCA.
  Running mode: sequential
  ! 10 columns in `obj` have no matching entry in `group` and will be left untouched.
  i feature1, feature2, feature3, feature4, feature5, ...
  Imputing 2 groups using PCA.
  Running mode: sequential
  ! 1 column in `obj` has no matching entry in `group` and will be left untouched.
  i e
  ! 3 columns in `obj` have no matching entry in `group` and will be left untouched.
  i d, e, f
  ! 15 columns in `obj` have no matching entry in `group` and will be left untouched.
  i feature6, feature7, feature8, feature9, feature10, ...
  Imputing 1 group using KNN.
  Running mode: sequential
  Saving _problems/test-pca_imp-7.R
  Saving _problems/test-pca_imp-116.R
  Saving _problems/test-pca_imp-179.R
  Step 1/2: Imputing
   Processing window 1 of 3
   Processing window 2 of 3
   Processing window 3 of 3
  Step 2/2: Averaging overlapping regions
  Step 1/2: Imputing
   Processing window 1 of 3
   Processing window 2 of 3
   Processing window 3 of 3
  Step 2/2: Averaging overlapping regions
  Step 1/2: Imputing
   Processing window 1 of 3
   Processing window 2 of 3
   Processing window 3 of 3
  Step 2/2: Averaging overlapping regions
  No features in `subset` detected. No imputation was performed.
  No columns to impute. Returning input unchanged.
  Dropping 1 window(s) covering no `subset` columns.
  Step 1/2: Imputing
   Processing window 1 of 2
   Processing window 2 of 2
  Step 2/2: Averaging overlapping regions
  Step 1/2: Imputing
   Processing window 1 of 3
   Processing window 2 of 3
   Processing window 3 of 3
  Step 2/2: Averaging overlapping regions
  Step 1/2: Imputing
   Processing window 1 of 3
   Processing window 2 of 3
   Processing window 3 of 3
  Step 2/2: Averaging overlapping regions
  Step 1/2: Imputing
   Processing window 1 of 3
   Processing window 2 of 3
   Processing window 3 of 3
  Step 2/2: Averaging overlapping regions
  Dropping 1 window(s) covering no `subset` columns.
  Step 1/2: Imputing
   Processing window 1 of 2
   Processing window 2 of 2
  Step 2/2: Averaging overlapping regions
  Dropping 1 window(s) covering no `subset` columns.
  Step 1/2: Imputing
   Processing window 1 of 2
   Processing window 2 of 2
  Step 2/2: Averaging overlapping regions
  Tuning `slide_imp()`
  Step 1/2: Resolving NA locations
  Running mode: sequential
  Step 2/2: Tuning
  Tuning `knn_imp()`
  Step 1/2: Resolving NA locations
  Running mode: sequential
  Step 2/2: Tuning
  Tuning `pca_imp()`
  Step 1/2: Resolving NA locations
  Running mode: sequential
  Step 2/2: Tuning
  Tuning custom function
  Step 1/2: Resolving NA locations
  Running mode: sequential
  Step 2/2: Tuning
  Tuning `slide_imp()`
  Step 1/2: Resolving NA locations
  Running mode: sequential
  Step 2/2: Tuning
  Tuning `knn_imp()`
  Step 1/2: Resolving NA locations
  Running mode: sequential
  Step 2/2: Tuning
  Tuning custom function
  Step 1/2: Resolving NA locations
  Running mode: sequential
  Step 2/2: Tuning
  Tuning `slide_imp()`
  Step 1/2: Resolving NA locations
  Running mode: sequential
  Step 2/2: Tuning
  Tuning custom function
  Step 1/2: Resolving NA locations
  Running mode: sequential
  Step 2/2: Tuning
  Tuning custom function
  Step 1/2: Resolving NA locations
  Running mode: sequential
  Step 2/2: Tuning
  Tuning custom function
  Step 1/2: Resolving NA locations
  Running mode: sequential
  Step 2/2: Tuning
  Tuning custom function
  Step 1/2: Resolving NA locations
  Running mode: sequential
  Step 2/2: Tuning
  Tuning custom function
  Step 1/2: Resolving NA locations
  Running mode: sequential
  Step 2/2: Tuning
  Tuning custom function
  Step 1/2: Resolving NA locations
  Running mode: sequential
  Step 2/2: Tuning
  Tuning custom function
  Step 1/2: Resolving NA locations
  Running mode: sequential
  Step 2/2: Tuning
  Tuning custom function
  Step 1/2: Resolving NA locations
  Running mode: sequential
  Step 2/2: Tuning
  Tuning custom function
  Step 1/2: Resolving NA locations
  Running mode: sequential
  Step 2/2: Tuning
  Step 1/2: Imputing
   Processing window 1 of 3
   Processing window 2 of 3
   Processing window 3 of 3
  Step 2/2: Averaging overlapping regions
  Step 1/2: Imputing
   Processing window 1 of 3
   Processing window 2 of 3
   Processing window 3 of 3
  Step 2/2: Averaging overlapping regions
  Tuning `slide_imp()`
  Step 1/2: Resolving NA locations
  Running mode: sequential
  Step 2/2: Tuning
  Tuning `slide_imp()`
  Step 1/2: Resolving NA locations
  Running mode: sequential
  Step 2/2: Tuning
  [ FAIL 3 | WARN 22 | SKIP 3 | PASS 496 ]
  
  ══ Skipped tests (3) ═══════════════════════════════════════════════════════════
  • Manual Testing Only (1): 'test-knn_imp.R:82:3'
  • empty test (1): 'test-group_imp.R:331:1'
  • manual testing with {slideimp.extra} on local machines only (1):
    'test-group_imp.R:678:3'
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error ('test-pca_imp.R:7:3'): same results as imputePCA ─────────────────────
  Error in `if ((criterion < threshold) && (nb.iter > 5)) nb.iter <- 0`: missing value where TRUE/FALSE needed
  Backtrace:
      ▆
   1. └─missMDA::imputePCA(to_test, ncp = 2, nb.init = 1, seed = 1234) at test-pca_imp.R:7:3
   2.   └─missMDA (local) impute(...)
  ── Error ('test-pca_imp.R:116:3'): same results as imputePCA, scale = FALSE ────
  Error in `if ((criterion < threshold) && (nb.iter > 5)) nb.iter <- 0`: missing value where TRUE/FALSE needed
  Backtrace:
      ▆
   1. └─missMDA::imputePCA(...) at test-pca_imp.R:116:3
   2.   └─missMDA (local) impute(...)
  ── Error ('test-pca_imp.R:176:3'): row.w = 'n_miss' matches missMDA::imputePCA with equivalent weights ──
  Error in `if (tol * ans$d[1] < eps) warning("convergence criterion below machine epsilon")`: missing value where TRUE/FALSE needed
  Backtrace:
      ▆
   1. └─missMDA::imputePCA(...) at test-pca_imp.R:176:3
   2.   └─missMDA (local) impute(...)
   3.     └─FactoMineR::svd.triplet(Xhat, row.w = row.w, ncp = ncp)
   4.       └─irlba::irlba(X, nu = ncp, nv = ncp)
  
  [ FAIL 3 | WARN 22 | SKIP 3 | PASS 496 ]
  Error:
  ! Test failures.
  Execution halted