Extract leverages of a PCA model

Share:

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

1
2
## 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

1
2
3
4
data(iris)
pcIr <- pca(iris[,1:4])
## versicolor has the lowest leverage
with(iris, plot(leverage(pcIr)~Species))

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.