plotprm {chemometrics} | R Documentation

## 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, ...)

### 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

    data(cereal)
    set.seed(123)
    res <- prm_cv(cereal$X,cereal$Y[,1],a=10,segments=4,plot.opt=FALSE)
    plotprm(res,cereal$Y[,1])

[Package chemometrics version 0.6 Index]