prm {chemometrics} | R Documentation |
Robust PLS by partial robust M-regression.
prm(X, y, a, fairct = 4, opt = "l1m")
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 by the coordinate-wise median |
M-regression is used to robustify PLS, with initial weights based on the FAIR weight function.
coef |
vector with regression coefficients |
wy |
vector of length(y) with residual weights |
wt |
vector of length(y) with weights for leverage |
scores |
matrix with PLS X-scores |
loadings |
matrix with PLS X-loadings |
fitted.values |
vector with fitted y-values |
Peter Filzmoser <P.Filzmoser@tuwien.ac.at>
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.
data(PAC) res <- prm(PAC$X,PAC$y,a=5)