leverage,pcaRes-method {pcaMethods} | R Documentation

## Extract leverages of a PCA model

### Description

The leverages of PCA model indicate how much influence each observation has on the PCA model. Observations with high leverage has caused the principal components to rotate towards them. It can be used to extract both "unimportant" observations as well as picking potential outliers.

### Usage

    ## S4 method for signature 'pcaRes'
    leverage(object)

### Arguments

`object` | a `pcaRes` object

### Details

Defined as Tr(T(T'T)^(-1)T')

### Value

The observation leverages as a numeric vector

### Author(s)

Henning Redestig

### References

Introduction to Multi- and Megavariate Data Analysis using Projection Methods (PCA and PLS), L. Eriksson, E. Johansson, N. Kettaneh-Wold and S. Wold, Umetrics 1999, p. 466

### Examples

    data(iris)
    pcIr <- pca(iris[,1:4])
    ## versicolor has the lowest leverage
    with(iris, plot(leverage(pcIr)~Species))

[Package pcaMethods version 1.76.0 Index]