R: Robust PLS

prm {chemometrics} | R Documentation

## Robust PLS

### Description

Robust PLS by partial robust M-regression.

### Usage

    prm(X, y, a, fairct = 4, opt = "l1m",usesvd=FALSE)

### Arguments

`X` | predictor matrix
`y` | response variable
`a` | number of PLS components
`fairct` | tuning constant, by default fairct=4
`opt` | if "l1m" the mean centering is done by the l1-median, otherwise if "median" the coordinate-wise median is taken
`usesvd` | if TRUE, SVD will be used if X has more columns than rows

### Details

M-regression is used to robustify PLS, with initial weights based on the FAIR weight function.

### Value

`coef` | vector with regression coefficients
`intercept` | coefficient for intercept
`wy` | vector of length(y) with residual weights
`wt` | vector of length(y) with weights for leverage
`w` | overall weights
`scores` | matrix with PLS X-scores
`loadings` | matrix with PLS X-loadings
`fitted.values` | vector with fitted y-values
`mx` | column means of X
`my` | mean of y

### Author(s)

Peter Filzmoser <P.Filzmoser@tuwien.ac.at>

### References

S. Serneels, C. Croux, P. Filzmoser, and P.J. Van Espen. Partial robust M-regression. Chemometrics and Intelligent Laboratory Systems, Vol. 79(1-2), pp. 55-64, 2005.

### See Also

`mvr`

### Examples

    data(PAC)
    res <- prm(PAC$X,PAC$y,a=5)

[Package chemometrics version 1.4.2 Index]