PRM: Robust PLS

In rpls: Robust Partial Least Squares

Description

Robust PLS by partial robust M-regression.

Usage

PRM(formula,data,a,wfunX,wfunY,center.type,scale.type,usesvd,numit,prec)

Arguments

formula	an object of class formula
data	a data frame which contains the variables given in formula
a	number of PLS components
wfunX	weight function to downweight leverage points; predefined weight funcktions "Fair", "Huber", "Tukey" and "Hampel" with respective tuning constants are passed via a list object, e.g. list("Fair",0.95)
wfunY	weight function to downweight residuals; predefined weight funcktions "Fair", "Huber", "Tukey" and "Hampel" with respective tuning constants are passed via a list object, e.g. list("Fair",0.95)
center.type	type of centering of the data in form of a string that matches an R function, e.g. "median"
scale.type	type of scaling for the data in form of a string that matches an R function, e.g. "qn" or alternatively "no" for no scaling
numit	the number of maximal iterations for the convergence of the coefficient estimates
prec	a value for the precision of estimation of the coefficients
usesvd	if TRUE singular value decomposition is performed; logical, default is FALSE

Details

M regression is used to robustify PLS. Employment of seperate weight functions for leverage points and residuals.

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

Author(s)

Peter Filzmoser <[e-mail]>

References

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

Examples

U <- c(rep(2,20), rep(5,30))
X <- replicate(6, U+rnorm(50))
beta <- c(rep(1, 3), rep(-1,3))
e <- c(rnorm(45,0,1.5),rnorm(5,-20,1))
y <- X%*%beta + e
d <- as.data.frame(X)
d$y <- y
res <- PRM(y~., data=d, 3, wfunX=list("Fair",0.95),
wfunY=list("Fair",0.95), center.type = "median",
scale.type = "no",usesvd = FALSE,
numit = 100, prec = 0.01)
res$coef

Example output

[1]  1.0310097  0.8927724  1.0044301 -1.1344546 -0.6339380 -1.4058849

rpls documentation built on July 8, 2020, 6:46 p.m.