Plot results from robust PLS
The predicted values and the residuals are shown for robust PLS using the optimal number of components.
plotprm(prmobj, y, ...)
prmobj |
resulting object from CV of robust PLS, see |
y |
vector with values of response variable |
... |
additional plot arguments |
Robust PLS based on partial robust M-regression is available at prm.
Here the function prm_cv has to be used first, applying cross-validation
with robust PLS. Then the result is taken by this routine and two plots are generated
for the optimal number of PLS components: The measured versus the predicted y, and
the predicted y versus the residuals.
A plot is generated.
Peter Filzmoser <P.Filzmoser@tuwien.ac.at>
K. Varmuza and P. Filzmoser: Introduction to Multivariate Statistical Analysis in Chemometrics. CRC Press, Boca Raton, FL, 2009.
data(cereal) set.seed(123) res <- prm_cv(cereal$X,cereal$Y[,1],a=5,segments=4,plot.opt=FALSE) plotprm(res,cereal$Y[,1])
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