R: Principal components analysis

PCA-methods {Cardinal}

R Documentation

Principal components analysis

Description

Performs principal components analysis efficiently on large datasets using implicitly restarted Lanczos bi-diagonalization (IRLBA) algorithm for approximate singular value decomposition of the data matrix.

Usage

    ## S4 method for signature 'SImageSet'
PCA(x, ncomp = 20,
        method = c("irlba", "nipals", "svd"),
        center = TRUE,
        scale = FALSE,
        iter.max = 100, ...)
    
    ## S4 method for signature 'PCA'
predict(object, newx, ...)

Arguments

`x`	The imaging dataset for which to calculate the principal components.
`ncomp`	The number of principal components to calculate.
`method`	The function used to calculate the singular value decomposition.
`center`	Should the data be centered first? This is passed to `scale`.
`scale`	Shoud the data be scaled first? This is passed to `scale`.
`iter.max`	The number of iterations to perform for the NIPALS algorithm.
`...`	Ignored.
`object`	The result of a previous call to `PCA`.
`newx`	An imaging dataset for which to calculate the principal components scores based on the aleady-calculated principal components loadings.

Value

An object of class PCA, which is a ResultSet, where each component of the resultData slot contains at least the following components:

scores:: A matrix with the principal component scores.
loadings:: A matrix with the principal component loadings.
sdev:: The standard deviations of the principal components.
method:: The method used to calculate the principal components.
ncomp:: The number of principal components calculated.
center:: The center of the dataset. Used for calculating principal components scores on new data.
scale:: The scaling factors for the dataset. Used for calculating principal components scores on new data.

Author(s)

Kylie A. Bemis

Examples

sset <- generateImage(diag(4), range=c(200, 300), step=1)

pca <- PCA(sset, ncomp=2)

[Package Cardinal version 2.0.4 Index]