choosePC: A function to choose principal components

In jr-leary7/jackknife: Bioinformatics and General Time-Saving Functions

Description

This function chooses an optimal number of principal components to explain the variance in your data. The amount of variance explained by the principal components is compared to the amount of variance explained by the principle components of a random reshuffing of the columns of the original dataframe.

Usage

choosePC(data, perms, plot, seed, center, scale)

Arguments

data	The data on which PCA will be performed
perms	How many permutations to perform on the data, default is 10
plot	Whether or not a plot should be returned comparing the two PCA, default is FALSE
seed	The seed to use for reshuffling dataframe columns, default is 629
center	Whether or not data should be centered prior to PCA, default is TRUE
scale	Whether or not data should be scaled prior to PCA, default is TRUE

Examples

choosePC(data = my_df, plot = TRUE, center = TRUE, scale = FALSE)

jr-leary7/jackknife documentation built on April 19, 2021, 2:18 a.m.