Running ‘testthat.R’ [11s/12s]
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(adproclus)
  > 
  > test_check("adproclus")
  ADPROCLUS solution 
  Setup: 
     number of clusters: 2 
     data format:  21 x 4 
     total time: 0.048 s 
     number of total starts: 6 
  Results Best Run: 
     explained variance: 0.976 
     time: 0.005 s 
     iterations to convergence: 2 
  
  A (cluster membership matrix): 
        Cl1 Cl2
   [1,]   1   1
   [2,]   1   1
   [3,]   1   1
   [4,]   1   0
   [5,]   1   0
   [6,]   1   0
   [7,]   1   0
   [8,]   1   0
   [9,]   1   0
  [10,]   1   0
  [ 11  rows were omitted ]
  
  P (profiles): 
      Air.Flow Water.Temp Acid.Conc. stack.loss
  Cl1   57.444     20.222     85.833     14.000
  Cl2   20.889      6.111      3.167     24.667
  
  Low Dimensional ADPROCLUS solution 
     number of clusters: 3 
     number of components:  2 
     data format:  21 x 4 
     total time: 0.066 s 
     number of total starts: 6 
  Results Best Run: 
     explained variance: 0.988 
     time: 0.009 s 
     iterations to convergence: 7 
  
  A (cluster membership matrix): 
        Cl1 Cl2 Cl3
   [1,]   1   1   0
   [2,]   1   1   0
   [3,]   1   1   0
   [4,]   1   0   1
   [5,]   1   0   1
   [6,]   1   0   1
   [7,]   1   0   1
   [8,]   1   0   1
   [9,]   1   0   1
  [10,]   0   1   0
  [ 11  rows were omitted ]
  
  C (profiles in terms of components - cluster by component): 
       Comp1   Comp2
  Cl1 21.445  33.674
  Cl2 98.800   8.596
  Cl3 89.240 -17.609
  
  B' (components by variables):  
        Air.Flow Water.Temp Acid.Conc. stack.loss
  Comp1    0.491      0.178      0.852      0.033
  Comp2    0.452      0.114     -0.315      0.827
  
  ADPROCLUS solution 
  Setup: 
     number of clusters: 2 
     data format:  21 x 4 
     total time: 0.05 s 
     number of total starts: 6 
  Results Best Run: 
     explained variance: 0.98 
     time: 0.01 s 
     iterations to convergence: 2 
  
  A (cluster membership matrix): 
       Cl1 Cl2
  [1,]   1   1
  [ 20  rows were omitted ]
  
  P (profiles): 
      Air.Flow Water.Temp Acid.Conc. stack.loss
  Cl1    57.44      20.22      85.83         14
  [ 1  rows were omitted ]
  
  ADPROCLUS solution 
  Setup: 
     number of clusters: 2 
     data format:  21 x 4 
     total time: 0.05 s 
     number of total starts: 6 
  Results Best Run: 
     explained variance: 0.98 
     time: 0.01 s 
     iterations to convergence: 2 
  
  A (cluster membership matrix): 
       Cl1 Cl2
  [1,]   1   1
  [ 20  rows were omitted ]
  
  P (profiles): 
      Air.Flow Water.Temp Acid.Conc. stack.loss
  Cl1    57.44      20.22      85.83         14
  [ 1  rows were omitted ]
  
  Low Dimensional ADPROCLUS solution 
     number of clusters: 3 
     number of components:  2 
     data format:  21 x 4 
     total time: 0.07 s 
     number of total starts: 6 
  Results Best Run: 
     explained variance: 0.99 
     time: 0.01 s 
     iterations to convergence: 7 
  
  A (cluster membership matrix): 
       Cl1 Cl2 Cl3
  [1,]   1   1   0
  [ 20  rows were omitted ]
  
  C (profiles in terms of components - cluster by component): 
      Comp1 Comp2
  Cl1 21.44 33.67
  [ 2  rows were omitted ]
  
  B' (components by variables):  
        Air.Flow Water.Temp Acid.Conc. stack.loss
  Comp1     0.49       0.18       0.85       0.03
  Comp2     0.45       0.11      -0.32       0.83
  
  Low Dimensional ADPROCLUS solution 
     number of clusters: 3 
     number of components:  2 
     data format:  21 x 4 
     total time: 0.07 s 
     number of total starts: 6 
  Results Best Run: 
     explained variance: 0.99 
     time: 0.01 s 
     iterations to convergence: 7 
  
  A (cluster membership matrix): 
       Cl1 Cl2 Cl3
  [1,]   1   1   0
  [ 20  rows were omitted ]
  
  C (profiles in terms of components - cluster by component): 
      Comp1 Comp2
  Cl1 21.44 33.67
  [ 2  rows were omitted ]
  
  B' (components by variables):  
        Air.Flow Water.Temp Acid.Conc. stack.loss
  Comp1     0.49       0.18       0.85       0.03
  Comp2     0.45       0.11      -0.32       0.83
  
  ADPROCLUS solution 
  Setup: 
     number of clusters: 2 
     data format:  21 x 4 
     total time: 0.05 s 
     number of total starts: 6 
  Results Best Run: 
     explained variance: 0.98 
     time: 0.01 s 
     iterations to convergence: 2 
  
  A (cluster membership matrix): 
       Cl1 Cl2
  [1,]   1   1
  [ 20  rows were omitted ]
  
  P (profiles): 
      Air.Flow Water.Temp Acid.Conc. stack.loss
  Cl1    57.44      20.22      85.83         14
  [ 1  rows were omitted ]
  
  Cluster sizes and overlaps:
       [,1] [,2] [,3]
  [1,]   21    3    0
  [2,]    3    3    0
  [3,]    0    0    0
   (diagonal entries: number of observations in a cluster)
   (off-diagonal entry [i,j]:  number of observations both in cluster i and j)
   (last row/column represents additional baseline cluster)
  
  Summary statistics of approximated model variables per cluster:
  
   Cl1 
       Air.Flow Water.Temp Acid.Conc. stack.loss
  Min     57.44      20.22      85.83      14.00
  Mean    60.43      21.10      86.29      17.52
  Max     78.33      26.33      89.00      38.67
  
   Cl2 
       Air.Flow Water.Temp Acid.Conc. stack.loss
  Min     78.33      26.33         89      38.67
  Mean    78.33      26.33         89      38.67
  Max     78.33      26.33         89      38.67
  Low Dimensional ADPROCLUS solution 
     number of clusters: 3 
     number of components:  2 
     data format:  21 x 4 
     total time: 0.06 s 
     number of total starts: 6 
  Results Best Run: 
     explained variance: 0.99 
     time: 0.01 s 
     iterations to convergence: 7 
  
  A (cluster membership matrix): 
       Cl1 Cl2 Cl3
  [1,]   1   1   0
  [ 20  rows were omitted ]
  
  C (profiles in terms of components - cluster by component): 
      Comp1 Comp2
  Cl1 21.44 33.67
  [ 2  rows were omitted ]
  
  B' (components by variables):  
        Air.Flow Water.Temp Acid.Conc. stack.loss
  Comp1     0.49       0.18       0.85       0.03
  Comp2     0.45       0.11      -0.32       0.83
  
  Cluster sizes and overlaps:
       [,1] [,2] [,3] [,4]
  [1,]   13    3   10    0
  [2,]    3   11    0    0
  [3,]   10    0   10    0
  [4,]    0    0    0    0
   (diagonal entries: number of observations in a cluster)
   (off-diagonal entry [i,j]:  number of observations both in cluster i and j)
   (last row/column represents additional baseline cluster)
  
  Summary statistics of approximated low dimensional components per cluster:
  
   Cl1 
        Comp1 Comp2
  Min  110.68 16.06
  Mean 112.89 22.11
  Max  120.24 42.27
  
   Cl2 
        Comp1 Comp2
  Min   98.80  8.60
  Mean 104.65 17.78
  Max  120.24 42.27
  
   Cl3 
        Comp1 Comp2
  Min  110.68 16.06
  Mean 110.68 16.06
  Max  110.68 16.06
  ADPROCLUS solution 
  Setup: 
     number of clusters: 2 
     data format:  21 x 4 
     total time: 0.05 s 
     number of total starts: 6 
  Results Best Run: 
     explained variance: 0.98 
     time: 0.01 s 
     iterations to convergence: 2 
  
  A (cluster membership matrix): 
       Cl1 Cl2
  [1,]   1   1
  [ 20  rows were omitted ]
  
  P (profiles): 
      Air.Flow Water.Temp Acid.Conc. stack.loss
  Cl1    57.44      20.22      85.83         14
  [ 1  rows were omitted ]
  
  Cluster sizes and overlaps:
       [,1] [,2] [,3]
  [1,]   21    3    0
  [2,]    3    3    0
  [3,]    0    0    0
   (diagonal entries: number of observations in a cluster)
   (off-diagonal entry [i,j]:  number of observations both in cluster i and j)
   (last row/column represents additional baseline cluster)
  
  Summary statistics of approximated model variables per cluster:
  
   Cl1 
       Air.Flow Water.Temp Acid.Conc. stack.loss
  Min     57.44      20.22      85.83      14.00
  Mean    60.43      21.10      86.29      17.52
  Max     78.33      26.33      89.00      38.67
  
   Cl2 
       Air.Flow Water.Temp Acid.Conc. stack.loss
  Min     78.33      26.33         89      38.67
  Mean    78.33      26.33         89      38.67
  Max     78.33      26.33         89      38.67
  ADPROCLUS solution 
  Setup: 
     number of clusters: 2 
     data format:  21 x 4 
     total time: 0.05 s 
     number of total starts: 6 
  Results Best Run: 
     explained variance: 0.98 
     time: 0.01 s 
     iterations to convergence: 2 
  
  A (cluster membership matrix): 
       Cl1 Cl2
  [1,]   1   1
  [ 20  rows were omitted ]
  
  P (profiles): 
      Air.Flow Water.Temp Acid.Conc. stack.loss
  Cl1    57.44      20.22      85.83         14
  [ 1  rows were omitted ]
  
  Cluster sizes and overlaps:
       [,1] [,2] [,3]
  [1,]   21    3    0
  [2,]    3    3    0
  [3,]    0    0    0
   (diagonal entries: number of observations in a cluster)
   (off-diagonal entry [i,j]:  number of observations both in cluster i and j)
   (last row/column represents additional baseline cluster)
  
  Summary statistics of approximated model variables per cluster:
  
   Cl1 
       Air.Flow Water.Temp Acid.Conc. stack.loss
  Min     57.44      20.22      85.83      14.00
  Mean    60.43      21.10      86.29      17.52
  Max     78.33      26.33      89.00      38.67
  
   Cl2 
       Air.Flow Water.Temp Acid.Conc. stack.loss
  Min     78.33      26.33         89      38.67
  Mean    78.33      26.33         89      38.67
  Max     78.33      26.33         89      38.67
  Low Dimensional ADPROCLUS solution 
     number of clusters: 3 
     number of components:  2 
     data format:  21 x 4 
     total time: 0.06 s 
     number of total starts: 6 
  Results Best Run: 
     explained variance: 0.99 
     time: 0.01 s 
     iterations to convergence: 7 
  
  A (cluster membership matrix): 
       Cl1 Cl2 Cl3
  [1,]   1   1   0
  [ 20  rows were omitted ]
  
  C (profiles in terms of components - cluster by component): 
      Comp1 Comp2
  Cl1 21.44 33.67
  [ 2  rows were omitted ]
  
  B' (components by variables):  
        Air.Flow Water.Temp Acid.Conc. stack.loss
  Comp1     0.49       0.18       0.85       0.03
  Comp2     0.45       0.11      -0.32       0.83
  
  Cluster sizes and overlaps:
       [,1] [,2] [,3] [,4]
  [1,]   13    3   10    0
  [2,]    3   11    0    0
  [3,]   10    0   10    0
  [4,]    0    0    0    0
   (diagonal entries: number of observations in a cluster)
   (off-diagonal entry [i,j]:  number of observations both in cluster i and j)
   (last row/column represents additional baseline cluster)
  
  Summary statistics of approximated low dimensional components per cluster:
  
   Cl1 
        Comp1 Comp2
  Min  110.68 16.06
  Mean 112.89 22.11
  Max  120.24 42.27
  
   Cl2 
        Comp1 Comp2
  Min   98.80  8.60
  Mean 104.65 17.78
  Max  120.24 42.27
  
   Cl3 
        Comp1 Comp2
  Min  110.68 16.06
  Mean 110.68 16.06
  Max  110.68 16.06
  Low Dimensional ADPROCLUS solution 
     number of clusters: 3 
     number of components:  2 
     data format:  21 x 4 
     total time: 0.06 s 
     number of total starts: 6 
  Results Best Run: 
     explained variance: 0.99 
     time: 0.01 s 
     iterations to convergence: 7 
  
  A (cluster membership matrix): 
       Cl1 Cl2 Cl3
  [1,]   1   1   0
  [ 20  rows were omitted ]
  
  C (profiles in terms of components - cluster by component): 
      Comp1 Comp2
  Cl1 21.44 33.67
  [ 2  rows were omitted ]
  
  B' (components by variables):  
        Air.Flow Water.Temp Acid.Conc. stack.loss
  Comp1     0.49       0.18       0.85       0.03
  Comp2     0.45       0.11      -0.32       0.83
  
  Cluster sizes and overlaps:
       [,1] [,2] [,3] [,4]
  [1,]   13    3   10    0
  [2,]    3   11    0    0
  [3,]   10    0   10    0
  [4,]    0    0    0    0
   (diagonal entries: number of observations in a cluster)
   (off-diagonal entry [i,j]:  number of observations both in cluster i and j)
   (last row/column represents additional baseline cluster)
  
  Summary statistics of approximated low dimensional components per cluster:
  
   Cl1 
        Comp1 Comp2
  Min  110.68 16.06
  Mean 112.89 22.11
  Max  120.24 42.27
  
   Cl2 
        Comp1 Comp2
  Min   98.80  8.60
  Mean 104.65 17.78
  Max  120.24 42.27
  
   Cl3 
        Comp1 Comp2
  Min  110.68 16.06
  Mean 110.68 16.06
  Max  110.68 16.06
  Saving _problems/test-visualize-52.R
  [ FAIL 1 | WARN 0 | SKIP 0 | PASS 74 ]
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Failure ('test-visualize.R:52:9'): Scree plots low dimensional ──────────────
  Expected `plot_scree_adpc(model_selection, grid = TRUE)` not to throw any conditions.
  Actually got a <rlang_message> with message:
    `geom_line()`: Each group consists of only one observation.
    i Do you need to adjust the group aesthetic?
  
  [ FAIL 1 | WARN 0 | SKIP 0 | PASS 74 ]
  Error:
  ! Test failures.
  Execution halted