Compute the PVE (percentage of variance explained) for each data set
Arguments
- dataset
A list of data sets for input
- list_score
A list of extracted scores by the corresponding algorithm
- list_component
A list of components comptuted by the corresponding algorithm
Examples
dataset = list(matrix(runif(5000, 1, 2), nrow = 100, ncol = 50))
comp_num = 2
res_sepPCA = sepPCA(dataset, comp_num)
pveSep(dataset, res_sepPCA$score_list, res_sepPCA$linked_component_list)
#> $dataset_No.1
#> subject_No.1 subject_No.2
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 1.269362 0.8822844
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 -1.020803 2.0057512
#> subject_No.3 subject_No.4
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 0.4119452 -1.2206348
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 -4.1562911 0.4497719
#> subject_No.5 subject_No.6
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 -0.5999940 -1.627559
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 0.1316427 1.396466
#> subject_No.7 subject_No.8
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 -2.858345 -1.116851
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 2.177357 1.272958
#> subject_No.9 subject_No.10
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 0.8852413 -3.566629
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 1.1510677 2.985520
#> subject_No.11
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 -0.6313001
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 4.5903746
#> subject_No.12
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 1.3058714
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 -0.6927287
#> subject_No.13
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 4.955926
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 -3.333619
#> subject_No.14
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 3.853117
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 2.222473
#> subject_No.15
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 0.6398326
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 -3.5834583
#> subject_No.16
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 -3.0828362
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 -0.2547361
#> subject_No.17
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 4.565988
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 -1.607503
#> subject_No.18
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 -4.559211
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 -2.126832
#> subject_No.19
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 -1.650555
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 -2.628202
#> subject_No.20
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 -1.446678
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 1.331196
#> subject_No.21
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 2.252430
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 1.065067
#> subject_No.22
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 -0.7077453
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 -2.8368271
#> subject_No.23
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 0.0807707
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 0.1465928
#> subject_No.24
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 -1.801513
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 -3.531221
#> subject_No.25
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 2.429377
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 1.248840
#> subject_No.26
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 -0.1593598
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 1.9840856
#> subject_No.27
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 2.2100434
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 0.6000955
#> subject_No.28
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 -1.22412
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 -1.89099
#> subject_No.29
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 0.07799926
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 0.25674549
#> subject_No.30
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 -1.766478
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 -1.290206
#> subject_No.31
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 -3.1370985
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 0.7140074
#> subject_No.32
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 3.251277
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 1.136505
#> subject_No.33
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 -3.669775
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 2.290494
#> subject_No.34
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 1.490325
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 2.988986
#> subject_No.35
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 0.188411662
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 -0.003828624
#> subject_No.36
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 -2.702655
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 2.134315
#> subject_No.37
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 1.305235
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 -1.949074
#> subject_No.38
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 0.8252565
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 2.8331756
#> subject_No.39
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 4.7394221
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 -0.5380792
#> subject_No.40
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 0.1082640
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 0.0982518
#> subject_No.41
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 4.1195389
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 -0.7289868
#> subject_No.42
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 0.2144923
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 -3.1639794
#> subject_No.43
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 -0.5894508
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 -0.8353333
#> subject_No.44
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 3.256380
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 3.352047
#> subject_No.45
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 0.4212448
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 4.1328608
#> subject_No.46
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 -2.162430
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 -1.268348
#> subject_No.47
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 0.9935125
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 -3.0822171
#> subject_No.48
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 -3.446872
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 -1.304178
#> subject_No.49
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 0.27131545
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 -0.01378271
#> subject_No.50
#> dataset_No.1_subcomp.1, PVE: 0.055324, PVE: 0.001872 -2.769463
#> dataset_No.1_subcomp.2, PVE: 0.047130, PVE: 0.001643 -2.832879
#>