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> # 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: 0.06 s 
   number of total starts: 6 
Results Best Run: 
   explained variance: 0.976 
   time: 0.02 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: 0.05 s 
   number of total starts: 6 
Results Best Run: 
   explained variance: 0.988 
   time: 0 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: 0.06 s 
   number of total starts: 6 
Results Best Run: 
   explained variance: 0.98 
   time: 0.02 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: 0.06 s 
   number of total starts: 6 
Results Best Run: 
   explained variance: 0.98 
   time: 0.02 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: 0.05 s 
   number of total starts: 6 
Results Best Run: 
   explained variance: 0.99 
   time: 0 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: 0.05 s 
   number of total starts: 6 
Results Best Run: 
   explained variance: 0.99 
   time: 0 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: 0.02 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: 0.05 s 
   number of total starts: 6 
Results Best Run: 
   explained variance: 0.99 
   time: 0 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: 0.02 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: 0.02 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: 0.05 s 
   number of total starts: 6 
Results Best Run: 
   explained variance: 0.99 
   time: 0 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: 0.05 s 
   number of total starts: 6 
Results Best Run: 
   explained variance: 0.99 
   time: 0 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
[ FAIL 0 | WARN 0 | SKIP 0 | PASS 75 ]
> 
> proc.time()
   user  system elapsed 
   8.89    0.78    9.67