Robust PLS
Robust PLS by partial robust M-regression.
prm(X, y, a, fairct = 4, opt = "l1m",usesvd=FALSE)
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 |
M-regression is used to robustify PLS, with initial weights based on the FAIR weight function.
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 |
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)
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