plotPCAVarPvalue {MatrixQCvis} | R Documentation |
The function 'plotPCAVarPvalue' plots the p-values of significances of principal components. Using the visual output, the optimal number of principal components can be selected.
plotPCAVarPvalue(var_x, var_perm)
var_x |
'numeric', measured variances |
var_perm |
'matrix', variances obtained by permutation |
Internal usage in 'shinyQC'.
'gg' object from 'ggplot'
Thomas Naake
x <- matrix(1:100, ncol = 10) var_x <- explVar(x = x, params = list(center = TRUE, scale = TRUE), type = "PCA") var_perm <- permuteExplVar(x = x, n = 100, center = TRUE, scale = TRUE) plotPCAVarPvalue(var_x = var_x, var_perm = var_perm)