ddmatrix-prcomp: Principal Components Analysis

In pbdDMAT: 'pbdR' Distributed Matrix Methods

Description

Performs the principal components analysis.

Usage

prcomp(x, ...)

## S4 method for signature 'ddmatrix'
prcomp(x, retx = TRUE, center = TRUE,
  scale. = FALSE, tol = NULL, ...)

Arguments

x	numeric distributed matrix.
...	Ignored.
retx	logical, indicates whether the rotated variables should be returned
center	logical value, determines whether or not columns are zero centered
scale.	logical value, determines whether or not columns are rescaled to unit variance
tol	a value indicating the magnitude below which components should be omitted. (Components are omitted if their standard deviations are less than or equal to tol times the standard deviation of the first component.) With the default null setting, no components are omitted. Other settings for tol could be tol = 0 or tol = sqrt(.Machine$double.eps), which would omit essentially constant components

Details

prcomp() performs the principal components analysis on the data matrix by taking the SVD. Sometimes core R and pbdDMAT will disagree slightly in what the rotated variables are because of how the SVD is caluclated.

Value

Returns a list.

pbdDMAT documentation built on May 1, 2019, 6:34 p.m.