plotPCAVarPvalue {MatrixQCvis}R Documentation

Plot p-values for the significance of principal components

Description

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.

Usage

plotPCAVarPvalue(var_x, var_perm)

Arguments

var_x

'numeric', measured variances

var_perm

'matrix', variances obtained by permutation

Details

Internal usage in 'shinyQC'.

Value

'gg' object from 'ggplot'

Author(s)

Thomas Naake

Examples

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)


[Package MatrixQCvis version 1.0.0 Index]