predict.pcaRes {pcaMethods}R Documentation

Predict values from PCA.

Description

This function extracts the predict values from a pcaRes object for the PCA methods SVD, Nipals, PPCA and BPCA

Newdata is first centered if the PCA model was and then scores (T) and data (X) is 'predicted' according to :

That=XnewP

Xhat=ThatP'

Missing values are set to zero before matrix multiplication to achieve NIPALS like treatment of missing values.

Usage

predict.pcaRes(object, newdata, pcs=nPcs(object), ...)

Arguments

object pcaRes the pcaRes object of interest.
newdata matrix new data with same number of columns as the used to compute object.
pcs numeric The number of PC's to consider
... Not passed on anywhere, included for S3 consistency.

Value

A list with the following components:

scores The predicted scores
x The predicted data

Author(s)

Henning Redestig <henning[at]psc.riken.jp>

Examples

data(iris)
hidden <- sample(nrow(iris), 50)
pcIr <- pca(iris[-hidden,1:4])
pcFull <- pca(iris[,1:4])
irisHat <- predict(pcIr, iris[hidden,1:4])
cor(irisHat$scores[,1], scores(pcFull)[hidden,1])

[Package pcaMethods version 1.22.0 Index]