PCA-methods: Principal components analysis

In Cardinal: A mass spectrometry imaging toolbox for statistical 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 'SparseImagingExperiment'
PCA(x, ncomp = 3, center = TRUE, scale = FALSE, ...)

## S4 method for signature 'PCA2'
predict(object, newx, ncomp, ...)

## S4 method for signature 'PCA2'
summary(object, ...)

## S4 method for signature 'SImageSet'
PCA(x, ncomp = 3,
    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 PCA2, which is a ImagingResult, or an object of class PCA, which is a ResultSet. Each elemnt of resultData slot contains at least the following components:

loadings:

A matrix with the principal component loadings.

scores:

A matrix with the principal component scores.

sdev:

The standard deviations of the principal components.

Author(s)

Kylie A. Bemis

See Also

OPLS, PLS, irlba, svd

Examples

setCardinalBPPARAM(SerialParam())

set.seed(1)
data <- simulateImage(preset=2, npeaks=20, dim=c(6,6),
    representation="centroid")

# project to FastMap components
pca <- PCA(data, ncomp=2)

# visualize first 2 components
image(pca, superpose=FALSE)

Cardinal documentation built on Nov. 8, 2020, 11:10 p.m.