chemometrics (version 1.4.2)

plotprm: Plot results from robust PLS

Description

The predicted values and the residuals are shown for robust PLS using the optimal number of components.

Usage

plotprm(prmobj, y, ...)

Value

A plot is generated.

Arguments

prmobj

resulting object from CV of robust PLS, see prm_cv

y

vector with values of response variable

...

additional plot arguments

Author

Peter Filzmoser <[e-mail]>

Details

Robust PLS based on partial robust M-regression is available at prm. Here the function prm_cv has to be used first, applying cross-validation with robust PLS. Then the result is taken by this routine and two plots are generated for the optimal number of PLS components: The measured versus the predicted y, and the predicted y versus the residuals.

References

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

See Also

prm

Examples

data(cereal)
set.seed(123)
res <- prm_cv(cereal$X,cereal$Y[,1],a=5,segments=4,plot.opt=FALSE)
plotprm(res,cereal$Y[,1])