plotprm: Plot results from robust PLS

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

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

Usage

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plotprm(prmobj, y, ...)

Arguments

prmobj

resulting object from CV of robust PLS, see prm_cv

y

vector with values of response variable

...

additional plot arguments

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.

Value

A plot is generated.

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

prm

Examples

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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])


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