chemometrics (version 0.5)

prm: 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

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

Details

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

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)