pcaVarexpl: PCA diagnostics for variables

In chemometrics: Multivariate Statistical Analysis in Chemometrics

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 <[e-mail]>

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)

chemometrics documentation built on Aug. 25, 2023, 5:18 p.m.