rpls (version 0.6.0)

PRM: Robust PLS

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

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

Details

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

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

# NOT RUN {
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
# }