R: PCA diagnostics for variables

pcaVarexpl {chemometrics} | R Documentation

## PCA diagnostics for variables

### Description

Diagnostics of PCA to see the explained variance for each variable.

### Usage

    pcaVarexpl(X, a, center = TRUE, scale = TRUE, plot = TRUE, ...)

### Arguments

`X` | numeric data frame or matrix
`a` | number of principal components
`center` | centring of X (FALSE or TRUE)
`scale` | scaling of X (FALSE or TRUE)
`plot` | if TRUE make plot with explained variance
`...` | additional graphics parameters, see `par`

### Details

For a desired number of principal components the percentage of explained variance is computed for each variable and plotted.

### Value

`ExplVar` | explained variance for each variable

### Author(s)

Peter Filzmoser <P.Filzmoser@tuwien.ac.at>

### References

K. Varmuza and P. Filzmoser: Introduction to Multivariate Statistical Analysis in Chemometrics. CRC Press, Boca Raton, FL, 2009.

### See Also

`princomp`

### Examples

    data(glass)
    res <- pcaVarexpl(glass,a=2)

[Package chemometrics version 1.4.2 Index]