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 |
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
Examples
data(glass)
res <- pcaVarexpl(glass,a=2)
[Package chemometrics version 1.4.4 Index]