PCA: Principal Component Analysis

In rettopnivek/PCAandEFA: Convenience Functions to Run PCA or EFA

Description

A convenience function that conducts a prinicipal components analysis (PCA) on the specified rows and columns of a data frame using R's base function princomp.

Usage

PCA(
  df,
  rows = NULL,
  columns = NULL,
  patterns = NULL,
  exclude = T,
  retain = NULL
)

Arguments

df	A data frame.
rows	An optional logical vector matching in length to the number of rows in 'df' indicating which rows to use for the PCA.
columns	An optional vector of column names in 'df' to use for the PCA.
patterns	An optional vector of character strings, incomplete patterns to use to identify either columns to include in or columns to exclude from the PCA.
exclude	Logical; if TRUE, the 'patterns' vector are strings to identify columns to exclude; otherwise the 'patterns' vector are strings to identify columns to include.
retain	Description.

Details

Once a PCA has been conducted, prinicipal component scores are computed for all observations.

Value

An object of class 'PCA'.

Examples

# Example of function
results = PCA( mtcars, patterns = c( 'p', 'r' ) )

rettopnivek/PCAandEFA documentation built on May 4, 2020, 12:08 a.m.