Kernel PLS Regression
Fits a PLS regression model with the kernel algorithm (Dayal & Macgregor, 1997).
kernelpls.fit2(X, Y, ncomp) ## S3 method for class 'kernelpls.fit2' predict(object,X, ...)
X |
Matrix of regressors |
Y |
Vector of a univariate outcome |
ncomp |
Number of components to be extracted |
object |
Object of class |
... |
Further arguments to be passed |
The same list as in
{pls::kernelpls.fit}
is produced.
In addition, R^2 measures are contained in
R2.
This code is a Rcpp translation of the original
pls::kernelpls.fit function from the pls package
(see Mevik & Wehrens, 2007).
Dayal, B., & Macgregor, J. F. (1997). Improved PLS algorithms. Journal of Chemometrics, 11(1), 73-85.
Mevik, B. H., & Wehrens, R. (2007). The pls package: Principal component and partial least squares regression in R. Journal of Statistical Software, 18, 1-24. doi: 10.18637/jss.v018.i02
See the pls package for further estimation algorithms.
## Not run: ############################################################################# # SIMULATED EXAMPLE 1: 300 cases on 100 variables ############################################################################# set.seed(789) library(mvtnorm) N <- 300 # number of cases p <- 100 # number of predictors rho1 <- .6 # correlations between predictors # simulate data Sigma <- base::diag(1-rho1,p) + rho1 X <- mvtnorm::rmvnorm( N, sigma=Sigma ) beta <- base::seq( 0, 1, len=p ) y <- ( X %*% beta )[,1] + stats::rnorm( N, sd=.6 ) Y <- base::matrix(y,nrow=N, ncol=1 ) # PLS regression res <- miceadds::kernelpls.fit2( X=X, Y=Y, ncomp=20 ) # predict new scores Xpred <- predict( res, X=X[1:10,] ) ############################################################################# # EXAMPLE 2: Dataset yarn from pls package ############################################################################# # use kernelpls.fit from pls package library(pls) data(yarn,package="pls") mod1 <- pls::kernelpls.fit( X=yarn$NIR, Y=yarn$density, ncomp=10 ) # use kernelpls.fit2 from miceadds package Y <- base::matrix( yarn$density, ncol=1 ) mod2 <- miceadds::kernelpls.fit2( X=yarn$NIR, Y=Y, ncomp=10 ) ## End(Not run)
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