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]