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This R package implements a flexible and powerful framework for penalized Partial Least Squares (PPLS), including:
lambda
,
ncomp
),It is particularly suited for high-dimensional, structured, and nonlinear regression problems, such as functional data or spectral data.
We recommend to install the CRAN version for a stable version of
ppls
.
install.packages("ppls")
Alternatively, install the development version from GitHub with:
# install.packages("pak") ## If necessary, install "pak" beforehand
::pak("vguillemot/ppls") pak
The following is an example of how use ppls
on the
cookie
dataset included in the package:
library(ppls)
# Load cookie data
data(cookie)
<- as.matrix(cookie$NIR)
X <- cookie$constituents$fat
y
# Fit a 2-component kernel PPLS
<- penalized.pls(
fit.kpls X = X, y = y,
kernel = TRUE,
ncomp = 2
)
# Predict on train data
<- new.penalized.pls(ppls = fit.kpls, Xtest = X, ytest = y)$ypred
yhat
# Plot predicted vs observed
plot(yhat[,1], y, xlab = "Fitted", ylab = "Observed", pch = 16, asp = 1)
abline(0, 1, col = "blue", lty = 2)
Kraemer, N., Boulesteix, A.-L., & Tutz, G. (2008).
Penalized Partial Least Squares with Applications to B-Spline Transformations and Functional Data.
Chemometrics and Intelligent Laboratory Systems, 94(1), 60–69.
https://doi.org/10.1016/j.chemolab.2008.06.009
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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