README.md
In vbonhomme/mevolCVP: Bla blah
mevolCVP
The goal of mevolCVP is to …
Installation
You will be able to install the (once) released version of mevolCVP from
CRAN with:
install.packages("mevolCVP")
so far you can (provinding remotes is installed):
remotes::install_github("vbonhomme/mevolCVP")
Example
This is a basic example which shows you how to solve a common problem:
library(mevolCVP) # load the package
set.seed(2329) # for the sake of replicability
mevol_CVP(pig$mat, pig$gp, nrep=2) # run mevolCVP with 2 iterations (speed purpose)
#> [1] "The analyses are done with 2 groups"
#> group
#> DP WB
#> 42 129
#> $CVoriginal
#> [1] 80.70175 81.28655 81.87135 86.54971 86.54971 88.30409 90.64327 90.05848
#> [9] 88.88889 90.05848 90.64327 92.39766 91.81287 91.81287 91.22807 91.22807
#> [17] 91.22807 91.22807 91.22807 91.81287 91.81287 91.22807 91.22807 91.22807
#> [25] 91.22807 90.64327 90.64327 90.05848 90.05848
#>
#> $CVbalanced
#> 2PCs 3PCs 4PCs 5PCs 6PCs 7PCs 8PCs 9PCs
#> [1,] 79.76190 78.57143 82.14286 82.14286 88.09524 88.09524 88.09524 86.90476
#> [2,] 78.57143 76.19048 80.95238 80.95238 80.95238 82.14286 83.33333 84.52381
#> 10PCs 11PCs 12PCs 13PCs 14PCs 15PCs 16PCs 17PCs
#> [1,] 85.71429 86.90476 91.66667 91.66667 91.66667 92.85714 91.66667 89.28571
#> [2,] 83.33333 86.90476 85.71429 84.52381 85.71429 88.09524 85.71429 84.52381
#> 18PCs 19PCs 20PCs 21PCs 22PCs 23PCs 24PCs 25PCs
#> [1,] 86.90476 86.90476 83.33333 85.71429 83.33333 86.90476 86.90476 85.71429
#> [2,] 84.52381 82.14286 82.14286 83.33333 83.33333 84.52381 83.33333 82.14286
#> 26PCs 27PCs 28PCs 29PCs 30PCs
#> [1,] 89.28571 89.28571 89.28571 88.09524 88.09524
#> [2,] 82.14286 83.33333 84.52381 80.95238 78.57143
#>
#> $CVrandom
#> 2PCs 3PCs 4PCs 5PCs 6PCs 7PCs 8PCs 9PCs
#> [1,] 75.4386 75.43860 75.4386 74.85380 74.85380 74.26901 74.26901 74.26901
#> [2,] 74.8538 74.26901 74.8538 77.19298 76.60819 76.02339 76.02339 75.43860
#> 10PCs 11PCs 12PCs 13PCs 14PCs 15PCs 16PCs 17PCs
#> [1,] 74.26901 73.68421 73.68421 73.68421 73.68421 72.51462 72.51462 71.92982
#> [2,] 77.19298 74.85380 74.26901 73.09942 74.85380 75.43860 75.43860 74.26901
#> 18PCs 19PCs 20PCs 21PCs 22PCs 23PCs 24PCs 25PCs
#> [1,] 71.34503 73.09942 71.92982 68.42105 67.83626 69.00585 69.00585 67.25146
#> [2,] 73.68421 73.09942 71.92982 71.92982 72.51462 73.09942 71.34503 71.34503
#> 26PCs 27PCs 28PCs 29PCs 30PCs
#> [1,] 67.25146 66.08187 67.25146 64.91228 67.25146
#> [2,] 69.59064 73.09942 72.51462 71.92982 69.59064
#>
#> $CVbalancedrandom
#> 2PCs 3PCs 4PCs 5PCs 6PCs 7PCs 8PCs 9PCs
#> [1,] 40.47619 51.19048 54.76190 53.57143 48.80952 53.57143 52.38095 52.38095
#> [2,] 59.52381 61.90476 58.33333 55.95238 60.71429 61.90476 59.52381 57.14286
#> 10PCs 11PCs 12PCs 13PCs 14PCs 15PCs 16PCs 17PCs
#> [1,] 48.80952 48.80952 53.57143 48.80952 52.38095 59.52381 59.52381 58.33333
#> [2,] 57.14286 57.14286 57.14286 54.76190 55.95238 53.57143 53.57143 52.38095
#> 18PCs 19PCs 20PCs 21PCs 22PCs 23PCs 24PCs 25PCs
#> [1,] 55.95238 57.14286 52.38095 57.14286 59.52381 59.52381 57.14286 61.90476
#> [2,] 48.80952 50.00000 50.00000 50.00000 51.19048 53.57143 48.80952 46.42857
#> 26PCs 27PCs 28PCs 29PCs 30PCs
#> [1,] 60.71429 55.95238 57.14286 57.14286 55.95238
#> [2,] 51.19048 46.42857 46.42857 52.38095 53.57143
#>
#> $CVsummary
#> mean-CVbalanced CI5%-CVbalanced CI95%-CVbalanced mean-CVrandom
#> 2PCs 79.16667 78.63095 79.70238 75.14620
#> 3PCs 77.38095 76.30952 78.45238 74.85380
#> 4PCs 81.54762 81.01190 82.08333 75.14620
#> 5PCs 81.54762 81.01190 82.08333 76.02339
#> 6PCs 84.52381 81.30952 87.73810 75.73099
#> 7PCs 85.11905 82.44048 87.79762 75.14620
#> 8PCs 85.71429 83.57143 87.85714 75.14620
#> 9PCs 85.71429 84.64286 86.78571 74.85380
#> 10PCs 84.52381 83.45238 85.59524 75.73099
#> 11PCs 86.90476 86.90476 86.90476 74.26901
#> 12PCs 88.69048 86.01190 91.36905 73.97661
#> 13PCs 88.09524 84.88095 91.30952 73.39181
#> 14PCs 88.69048 86.01190 91.36905 74.26901
#> 15PCs 90.47619 88.33333 92.61905 73.97661
#> 16PCs 88.69048 86.01190 91.36905 73.97661
#> 17PCs 86.90476 84.76190 89.04762 73.09942
#> 18PCs 85.71429 84.64286 86.78571 72.51462
#> 19PCs 84.52381 82.38095 86.66667 73.09942
#> 20PCs 82.73810 82.20238 83.27381 71.92982
#> 21PCs 84.52381 83.45238 85.59524 70.17544
#> 22PCs 83.33333 83.33333 83.33333 70.17544
#> 23PCs 85.71429 84.64286 86.78571 71.05263
#> 24PCs 85.11905 83.51190 86.72619 70.17544
#> 25PCs 83.92857 82.32143 85.53571 69.29825
#> 26PCs 85.71429 82.50000 88.92857 68.42105
#> 27PCs 86.30952 83.63095 88.98810 69.59064
#> 28PCs 86.90476 84.76190 89.04762 69.88304
#> 29PCs 84.52381 81.30952 87.73810 68.42105
#> 30PCs 83.33333 79.04762 87.61905 68.42105
#> CI5%-CVrandom CI95%-CVrandom mean-CVbalancedrandom CI5%-CVbalancedrandom
#> 2PCs 74.88304 75.40936 50.00000 41.42857
#> 3PCs 74.32749 75.38012 56.54762 51.72619
#> 4PCs 74.88304 75.40936 56.54762 54.94048
#> 5PCs 74.97076 77.07602 54.76190 53.69048
#> 6PCs 74.94152 76.52047 54.76190 49.40476
#> 7PCs 74.35673 75.93567 57.73810 53.98810
#> 8PCs 74.35673 75.93567 55.95238 52.73810
#> 9PCs 74.32749 75.38012 54.76190 52.61905
#> 10PCs 74.41520 77.04678 52.97619 49.22619
#> 11PCs 73.74269 74.79532 52.97619 49.22619
#> 12PCs 73.71345 74.23977 55.35714 53.75000
#> 13PCs 73.12865 73.65497 51.78571 49.10714
#> 14PCs 73.74269 74.79532 54.16667 52.55952
#> 15PCs 72.66082 75.29240 56.54762 53.86905
#> 16PCs 72.66082 75.29240 56.54762 53.86905
#> 17PCs 72.04678 74.15205 55.35714 52.67857
#> 18PCs 71.46199 73.56725 52.38095 49.16667
#> 19PCs 73.09942 73.09942 53.57143 50.35714
#> 20PCs 71.92982 71.92982 51.19048 50.11905
#> 21PCs 68.59649 71.75439 53.57143 50.35714
#> 22PCs 68.07018 72.28070 55.35714 51.60714
#> 23PCs 69.21053 72.89474 56.54762 53.86905
#> 24PCs 69.12281 71.22807 52.97619 49.22619
#> 25PCs 67.45614 71.14035 54.16667 47.20238
#> 26PCs 67.36842 69.47368 55.95238 51.66667
#> 27PCs 66.43275 72.74854 51.19048 46.90476
#> 28PCs 67.51462 72.25146 51.78571 46.96429
#> 29PCs 65.26316 71.57895 54.76190 52.61905
#> 30PCs 67.36842 69.47368 54.76190 53.69048
#> CI95%-CVbalancedrandom
#> 2PCs 58.57143
#> 3PCs 61.36905
#> 4PCs 58.15476
#> 5PCs 55.83333
#> 6PCs 60.11905
#> 7PCs 61.48810
#> 8PCs 59.16667
#> 9PCs 56.90476
#> 10PCs 56.72619
#> 11PCs 56.72619
#> 12PCs 56.96429
#> 13PCs 54.46429
#> 14PCs 55.77381
#> 15PCs 59.22619
#> 16PCs 59.22619
#> 17PCs 58.03571
#> 18PCs 55.59524
#> 19PCs 56.78571
#> 20PCs 52.26190
#> 21PCs 56.78571
#> 22PCs 59.10714
#> 23PCs 59.22619
#> 24PCs 56.72619
#> 25PCs 61.13095
#> 26PCs 60.23810
#> 27PCs 55.47619
#> 28PCs 56.60714
#> 29PCs 56.90476
#> 30PCs 55.83333
You can also work on a single column:
mevol_CVP(pig$mat[, 1], pig$gp, nrep=2)
#> [1] "The analyses are done with 2 groups"
#> group
#> DP WB
#> 42 129
#> $CVoriginal
#> [1] 81.28655
#>
#> $CVbalanced
#> 2PCs 3PCs 4PCs 5PCs 6PCs 7PCs 8PCs 9PCs 10PCs 11PCs 12PCs 13PCs 14PCs
#> [1,] 76.19048 NA NA NA NA NA NA NA NA NA NA NA NA
#> [2,] 73.80952 NA NA NA NA NA NA NA NA NA NA NA NA
#> 15PCs 16PCs 17PCs 18PCs 19PCs 20PCs 21PCs 22PCs 23PCs 24PCs 25PCs 26PCs
#> [1,] NA NA NA NA NA NA NA NA NA NA NA NA
#> [2,] NA NA NA NA NA NA NA NA NA NA NA NA
#> 27PCs 28PCs 29PCs 30PCs 31PCs
#> [1,] NA NA NA NA NA
#> [2,] NA NA NA NA NA
#>
#> $CVrandom
#> 2PCs 3PCs 4PCs 5PCs 6PCs 7PCs 8PCs 9PCs 10PCs 11PCs 12PCs 13PCs 14PCs
#> [1,] 74.8538 NA NA NA NA NA NA NA NA NA NA NA NA
#> [2,] 75.4386 NA NA NA NA NA NA NA NA NA NA NA NA
#> 15PCs 16PCs 17PCs 18PCs 19PCs 20PCs 21PCs 22PCs 23PCs 24PCs 25PCs 26PCs
#> [1,] NA NA NA NA NA NA NA NA NA NA NA NA
#> [2,] NA NA NA NA NA NA NA NA NA NA NA NA
#> 27PCs 28PCs 29PCs 30PCs 31PCs
#> [1,] NA NA NA NA NA
#> [2,] NA NA NA NA NA
#>
#> $CVbalancedrandom
#> 2PCs 3PCs 4PCs 5PCs 6PCs 7PCs 8PCs 9PCs 10PCs 11PCs 12PCs 13PCs 14PCs
#> [1,] 51.19048 NA NA NA NA NA NA NA NA NA NA NA NA
#> [2,] 57.14286 NA NA NA NA NA NA NA NA NA NA NA NA
#> 15PCs 16PCs 17PCs 18PCs 19PCs 20PCs 21PCs 22PCs 23PCs 24PCs 25PCs 26PCs
#> [1,] NA NA NA NA NA NA NA NA NA NA NA NA
#> [2,] NA NA NA NA NA NA NA NA NA NA NA NA
#> 27PCs 28PCs 29PCs 30PCs 31PCs
#> [1,] NA NA NA NA NA
#> [2,] NA NA NA NA NA
#>
#> $CVsummary
#> mean-CVbalanced CI5%-CVbalanced CI95%-CVbalanced mean-CVrandom
#> 2PCs 75 73.92857 76.07143 75.1462
#> CI5%-CVrandom CI95%-CVrandom mean-CVbalancedrandom CI5%-CVbalancedrandom
#> 2PCs 74.88304 75.40936 54.16667 51.4881
#> CI95%-CVbalancedrandom
#> 2PCs 56.84524
More to come.
vbonhomme/mevolCVP documentation built on Aug. 24, 2024, 12:49 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
mevolCVP
The goal of mevolCVP is to …
Installation
You will be able to install the (once) released version of mevolCVP from CRAN with:
install.packages("mevolCVP")
so far you can (provinding remotes is installed):
remotes::install_github("vbonhomme/mevolCVP")
Example
This is a basic example which shows you how to solve a common problem:
library(mevolCVP) # load the package
set.seed(2329) # for the sake of replicability
mevol_CVP(pig$mat, pig$gp, nrep=2) # run mevolCVP with 2 iterations (speed purpose)
#> [1] "The analyses are done with 2 groups"
#> group
#> DP WB
#> 42 129
#> $CVoriginal
#> [1] 80.70175 81.28655 81.87135 86.54971 86.54971 88.30409 90.64327 90.05848
#> [9] 88.88889 90.05848 90.64327 92.39766 91.81287 91.81287 91.22807 91.22807
#> [17] 91.22807 91.22807 91.22807 91.81287 91.81287 91.22807 91.22807 91.22807
#> [25] 91.22807 90.64327 90.64327 90.05848 90.05848
#>
#> $CVbalanced
#> 2PCs 3PCs 4PCs 5PCs 6PCs 7PCs 8PCs 9PCs
#> [1,] 79.76190 78.57143 82.14286 82.14286 88.09524 88.09524 88.09524 86.90476
#> [2,] 78.57143 76.19048 80.95238 80.95238 80.95238 82.14286 83.33333 84.52381
#> 10PCs 11PCs 12PCs 13PCs 14PCs 15PCs 16PCs 17PCs
#> [1,] 85.71429 86.90476 91.66667 91.66667 91.66667 92.85714 91.66667 89.28571
#> [2,] 83.33333 86.90476 85.71429 84.52381 85.71429 88.09524 85.71429 84.52381
#> 18PCs 19PCs 20PCs 21PCs 22PCs 23PCs 24PCs 25PCs
#> [1,] 86.90476 86.90476 83.33333 85.71429 83.33333 86.90476 86.90476 85.71429
#> [2,] 84.52381 82.14286 82.14286 83.33333 83.33333 84.52381 83.33333 82.14286
#> 26PCs 27PCs 28PCs 29PCs 30PCs
#> [1,] 89.28571 89.28571 89.28571 88.09524 88.09524
#> [2,] 82.14286 83.33333 84.52381 80.95238 78.57143
#>
#> $CVrandom
#> 2PCs 3PCs 4PCs 5PCs 6PCs 7PCs 8PCs 9PCs
#> [1,] 75.4386 75.43860 75.4386 74.85380 74.85380 74.26901 74.26901 74.26901
#> [2,] 74.8538 74.26901 74.8538 77.19298 76.60819 76.02339 76.02339 75.43860
#> 10PCs 11PCs 12PCs 13PCs 14PCs 15PCs 16PCs 17PCs
#> [1,] 74.26901 73.68421 73.68421 73.68421 73.68421 72.51462 72.51462 71.92982
#> [2,] 77.19298 74.85380 74.26901 73.09942 74.85380 75.43860 75.43860 74.26901
#> 18PCs 19PCs 20PCs 21PCs 22PCs 23PCs 24PCs 25PCs
#> [1,] 71.34503 73.09942 71.92982 68.42105 67.83626 69.00585 69.00585 67.25146
#> [2,] 73.68421 73.09942 71.92982 71.92982 72.51462 73.09942 71.34503 71.34503
#> 26PCs 27PCs 28PCs 29PCs 30PCs
#> [1,] 67.25146 66.08187 67.25146 64.91228 67.25146
#> [2,] 69.59064 73.09942 72.51462 71.92982 69.59064
#>
#> $CVbalancedrandom
#> 2PCs 3PCs 4PCs 5PCs 6PCs 7PCs 8PCs 9PCs
#> [1,] 40.47619 51.19048 54.76190 53.57143 48.80952 53.57143 52.38095 52.38095
#> [2,] 59.52381 61.90476 58.33333 55.95238 60.71429 61.90476 59.52381 57.14286
#> 10PCs 11PCs 12PCs 13PCs 14PCs 15PCs 16PCs 17PCs
#> [1,] 48.80952 48.80952 53.57143 48.80952 52.38095 59.52381 59.52381 58.33333
#> [2,] 57.14286 57.14286 57.14286 54.76190 55.95238 53.57143 53.57143 52.38095
#> 18PCs 19PCs 20PCs 21PCs 22PCs 23PCs 24PCs 25PCs
#> [1,] 55.95238 57.14286 52.38095 57.14286 59.52381 59.52381 57.14286 61.90476
#> [2,] 48.80952 50.00000 50.00000 50.00000 51.19048 53.57143 48.80952 46.42857
#> 26PCs 27PCs 28PCs 29PCs 30PCs
#> [1,] 60.71429 55.95238 57.14286 57.14286 55.95238
#> [2,] 51.19048 46.42857 46.42857 52.38095 53.57143
#>
#> $CVsummary
#> mean-CVbalanced CI5%-CVbalanced CI95%-CVbalanced mean-CVrandom
#> 2PCs 79.16667 78.63095 79.70238 75.14620
#> 3PCs 77.38095 76.30952 78.45238 74.85380
#> 4PCs 81.54762 81.01190 82.08333 75.14620
#> 5PCs 81.54762 81.01190 82.08333 76.02339
#> 6PCs 84.52381 81.30952 87.73810 75.73099
#> 7PCs 85.11905 82.44048 87.79762 75.14620
#> 8PCs 85.71429 83.57143 87.85714 75.14620
#> 9PCs 85.71429 84.64286 86.78571 74.85380
#> 10PCs 84.52381 83.45238 85.59524 75.73099
#> 11PCs 86.90476 86.90476 86.90476 74.26901
#> 12PCs 88.69048 86.01190 91.36905 73.97661
#> 13PCs 88.09524 84.88095 91.30952 73.39181
#> 14PCs 88.69048 86.01190 91.36905 74.26901
#> 15PCs 90.47619 88.33333 92.61905 73.97661
#> 16PCs 88.69048 86.01190 91.36905 73.97661
#> 17PCs 86.90476 84.76190 89.04762 73.09942
#> 18PCs 85.71429 84.64286 86.78571 72.51462
#> 19PCs 84.52381 82.38095 86.66667 73.09942
#> 20PCs 82.73810 82.20238 83.27381 71.92982
#> 21PCs 84.52381 83.45238 85.59524 70.17544
#> 22PCs 83.33333 83.33333 83.33333 70.17544
#> 23PCs 85.71429 84.64286 86.78571 71.05263
#> 24PCs 85.11905 83.51190 86.72619 70.17544
#> 25PCs 83.92857 82.32143 85.53571 69.29825
#> 26PCs 85.71429 82.50000 88.92857 68.42105
#> 27PCs 86.30952 83.63095 88.98810 69.59064
#> 28PCs 86.90476 84.76190 89.04762 69.88304
#> 29PCs 84.52381 81.30952 87.73810 68.42105
#> 30PCs 83.33333 79.04762 87.61905 68.42105
#> CI5%-CVrandom CI95%-CVrandom mean-CVbalancedrandom CI5%-CVbalancedrandom
#> 2PCs 74.88304 75.40936 50.00000 41.42857
#> 3PCs 74.32749 75.38012 56.54762 51.72619
#> 4PCs 74.88304 75.40936 56.54762 54.94048
#> 5PCs 74.97076 77.07602 54.76190 53.69048
#> 6PCs 74.94152 76.52047 54.76190 49.40476
#> 7PCs 74.35673 75.93567 57.73810 53.98810
#> 8PCs 74.35673 75.93567 55.95238 52.73810
#> 9PCs 74.32749 75.38012 54.76190 52.61905
#> 10PCs 74.41520 77.04678 52.97619 49.22619
#> 11PCs 73.74269 74.79532 52.97619 49.22619
#> 12PCs 73.71345 74.23977 55.35714 53.75000
#> 13PCs 73.12865 73.65497 51.78571 49.10714
#> 14PCs 73.74269 74.79532 54.16667 52.55952
#> 15PCs 72.66082 75.29240 56.54762 53.86905
#> 16PCs 72.66082 75.29240 56.54762 53.86905
#> 17PCs 72.04678 74.15205 55.35714 52.67857
#> 18PCs 71.46199 73.56725 52.38095 49.16667
#> 19PCs 73.09942 73.09942 53.57143 50.35714
#> 20PCs 71.92982 71.92982 51.19048 50.11905
#> 21PCs 68.59649 71.75439 53.57143 50.35714
#> 22PCs 68.07018 72.28070 55.35714 51.60714
#> 23PCs 69.21053 72.89474 56.54762 53.86905
#> 24PCs 69.12281 71.22807 52.97619 49.22619
#> 25PCs 67.45614 71.14035 54.16667 47.20238
#> 26PCs 67.36842 69.47368 55.95238 51.66667
#> 27PCs 66.43275 72.74854 51.19048 46.90476
#> 28PCs 67.51462 72.25146 51.78571 46.96429
#> 29PCs 65.26316 71.57895 54.76190 52.61905
#> 30PCs 67.36842 69.47368 54.76190 53.69048
#> CI95%-CVbalancedrandom
#> 2PCs 58.57143
#> 3PCs 61.36905
#> 4PCs 58.15476
#> 5PCs 55.83333
#> 6PCs 60.11905
#> 7PCs 61.48810
#> 8PCs 59.16667
#> 9PCs 56.90476
#> 10PCs 56.72619
#> 11PCs 56.72619
#> 12PCs 56.96429
#> 13PCs 54.46429
#> 14PCs 55.77381
#> 15PCs 59.22619
#> 16PCs 59.22619
#> 17PCs 58.03571
#> 18PCs 55.59524
#> 19PCs 56.78571
#> 20PCs 52.26190
#> 21PCs 56.78571
#> 22PCs 59.10714
#> 23PCs 59.22619
#> 24PCs 56.72619
#> 25PCs 61.13095
#> 26PCs 60.23810
#> 27PCs 55.47619
#> 28PCs 56.60714
#> 29PCs 56.90476
#> 30PCs 55.83333
You can also work on a single column:
mevol_CVP(pig$mat[, 1], pig$gp, nrep=2)
#> [1] "The analyses are done with 2 groups"
#> group
#> DP WB
#> 42 129
#> $CVoriginal
#> [1] 81.28655
#>
#> $CVbalanced
#> 2PCs 3PCs 4PCs 5PCs 6PCs 7PCs 8PCs 9PCs 10PCs 11PCs 12PCs 13PCs 14PCs
#> [1,] 76.19048 NA NA NA NA NA NA NA NA NA NA NA NA
#> [2,] 73.80952 NA NA NA NA NA NA NA NA NA NA NA NA
#> 15PCs 16PCs 17PCs 18PCs 19PCs 20PCs 21PCs 22PCs 23PCs 24PCs 25PCs 26PCs
#> [1,] NA NA NA NA NA NA NA NA NA NA NA NA
#> [2,] NA NA NA NA NA NA NA NA NA NA NA NA
#> 27PCs 28PCs 29PCs 30PCs 31PCs
#> [1,] NA NA NA NA NA
#> [2,] NA NA NA NA NA
#>
#> $CVrandom
#> 2PCs 3PCs 4PCs 5PCs 6PCs 7PCs 8PCs 9PCs 10PCs 11PCs 12PCs 13PCs 14PCs
#> [1,] 74.8538 NA NA NA NA NA NA NA NA NA NA NA NA
#> [2,] 75.4386 NA NA NA NA NA NA NA NA NA NA NA NA
#> 15PCs 16PCs 17PCs 18PCs 19PCs 20PCs 21PCs 22PCs 23PCs 24PCs 25PCs 26PCs
#> [1,] NA NA NA NA NA NA NA NA NA NA NA NA
#> [2,] NA NA NA NA NA NA NA NA NA NA NA NA
#> 27PCs 28PCs 29PCs 30PCs 31PCs
#> [1,] NA NA NA NA NA
#> [2,] NA NA NA NA NA
#>
#> $CVbalancedrandom
#> 2PCs 3PCs 4PCs 5PCs 6PCs 7PCs 8PCs 9PCs 10PCs 11PCs 12PCs 13PCs 14PCs
#> [1,] 51.19048 NA NA NA NA NA NA NA NA NA NA NA NA
#> [2,] 57.14286 NA NA NA NA NA NA NA NA NA NA NA NA
#> 15PCs 16PCs 17PCs 18PCs 19PCs 20PCs 21PCs 22PCs 23PCs 24PCs 25PCs 26PCs
#> [1,] NA NA NA NA NA NA NA NA NA NA NA NA
#> [2,] NA NA NA NA NA NA NA NA NA NA NA NA
#> 27PCs 28PCs 29PCs 30PCs 31PCs
#> [1,] NA NA NA NA NA
#> [2,] NA NA NA NA NA
#>
#> $CVsummary
#> mean-CVbalanced CI5%-CVbalanced CI95%-CVbalanced mean-CVrandom
#> 2PCs 75 73.92857 76.07143 75.1462
#> CI5%-CVrandom CI95%-CVrandom mean-CVbalancedrandom CI5%-CVbalancedrandom
#> 2PCs 74.88304 75.40936 54.16667 51.4881
#> CI95%-CVbalancedrandom
#> 2PCs 56.84524
More to come.
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