| plotprm {chemometrics} | R Documentation |
Plot results from robust PLS
Description
The predicted values and the residuals are shown for robust PLS using the optimal number of components.
Usage
plotprm(prmobj, y, ...)
Arguments
prmobj |
resulting object from CV of robust PLS, see |
y |
vector with values of response variable |
... |
additional plot arguments |
Details
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.
Value
A plot is generated.
Author(s)
Peter Filzmoser <P.Filzmoser@tuwien.ac.at>
References
K. Varmuza and P. Filzmoser: Introduction to Multivariate Statistical Analysis in Chemometrics. CRC Press, Boca Raton, FL, 2009.
See Also
Examples
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])