fitted-methods {pcaMethods} | R Documentation

## Extract fitted values from PCA.

### Description

Fitted values of a PCA model

### Usage

    ## S3 method for class 'pcaRes'
    fitted(object, data = NULL, nPcs = nP(object),
      pre = TRUE, post = TRUE, ...)

    ## S4 method for signature 'pcaRes'
    fitted(object, data = NULL, nPcs = nP(object),
      pre = TRUE, post = TRUE, ...)

### Arguments

`object` | the `pcaRes` object of interest.
`data` | For standard PCA methods this can safely be left null to get scores x loadings but if set, then the scores are obtained by projecting provided data onto the loadings. If data contains missing values the result will be all NA. Non-linear PCA is an exception, here if data is NULL then data is set to the completeObs and propaged through the network.
`nPcs` | The number of PC's to consider
`pre` | pre-process `data` based on the pre-processing chosen for the PCA model
`post` | unpre-process the final data (add the center back etc to get the final estimate)
`...` | Not used

### Details

This function extracts the fitted values from a pcaResobject. For PCA methods like SVD, Nipals, PPCA etc this is basically just the scores multipled by the loadings and adjusted for pre-processing. for non-linear PCA the original data is propagated through the network to obtain the approximated data.

### Value

A matrix representing the fitted data

### Author(s)

Henning Redestig

### Examples

    pc <- pca(iris[,1:4], nPcs=4, center=TRUE, scale="uv")
    sum( (fitted(pc) - iris[,1:4])^2 )

[Package pcaMethods version 1.72.0 Index]