PCA: Perform Principal Component Analysis (PCA)

Description Usage Arguments Details Value Author(s) See Also Examples

Description

Perform principal components analysis on a data matrix and return the results as an object of class coords.

Usage

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  PCA(x, n.comp, scale = FALSE, compute.scores = TRUE)

Arguments

x

A data matrix, rows are observations, columns are variables.

n.comp

How many principal components to compute.

scale

Whether to standardize the columns before doing PCA.

compute.scores

Whether to compute the scores (i.e. x in the new basis).

Details

This function performs Principal Component Analysis (PCA) on the data. Variables are always centred before the PCA is performed and, if scale is set, the variables will also be rescaled to unit variance.

If compute.scores is set to FALSE, only the information required for the toPC() and fromPC() to work is stored in the returned coords object; otherwise the scores will be stored in the $y field of the coords object.

The PCA() function is an alternative to the prcomp() command from the standard library. The main advantage of PCA() is that the coords class provides functions to convert between the original basis and the principal component basis.

Value

An object of class coords, with the following additional components added:

loadings

the loadings, each column is one of the new basis vectors

y

if compute.scores==TRUE, this is x expressed in the new basis

var

the variance of the data along each of the new basis vectors

total.var

the total variance of the data

Author(s)

Jochen Voss <voss@seehuhn.de>

See Also

coords; alternative implementations: prcomp, princomp

Examples

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  pc <- PCA(iris[, 1:4], scale = TRUE, n.comp = 2)
  pc
  plot(pc$y, col=iris$Species)

jvcoords documentation built on May 2, 2019, 2:10 p.m.