predict-methods {pcaMethods} | R Documentation

## 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 : That=XnewP Xhat=ThatP'. 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])

[Package pcaMethods version 1.72.0 Index]