predict-methods: Predict values from PCA.

In hredestig/pcaMethods: A collection of PCA methods

Predict values from PCA.

Description

Predict data using PCA model

Usage

## S3 method for class 'pcaRes'
predict(object, newdata, pcs = nP(object), pre = TRUE,
  post = TRUE, ...)

## S4 method for signature 'pcaRes'
predict(object, newdata, pcs = nP(object),
  pre = TRUE, post = TRUE, ...)

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
pre	pre-process newdata based on the pre-processing chosen for the PCA model
post	unpre-process the final data (add the center back etc)
...	Not passed on anywhere, included for S3 consistency.

Details

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 : \hat{T}=X_{new}P \hat{X}_{new}=\hat{T}P'. Missing values are set to zero before matrix multiplication to achieve NIPALS like treatment of missing values.

Value

A list with the following components:

scores	The predicted scores
x	The predicted data

Author(s)

Henning Redestig

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])

hredestig/pcaMethods documentation built on Sept. 30, 2023, 10:38 a.m.