miceadds (version 1.5-0)

kernelpls.fit2: Kernel PLS Regression

Description

Fits a PLS regression model with the kernel algorithm (Dayal & Macgregor, 1997).

Usage

kernelpls.fit2(X, Y, ncomp)

## S3 method for class 'kernelpls.fit2':
predict(object,X, ...)

Arguments

X

Matrix of regressors

Y

Vector of a univariate outcome

ncomp

Number of components to be extracted

object

Object of class kernelpls.fit2

...

Further arguments to be passed

Value

• The same list as in pls::kernelpls.fit of the pls package is produced. In addition, $R^2$ measures are contained in R2.

References

Dayal, B., & Macgregor, J. F. (1997). Improved PLS algorithms. Journal of Chemometrics, 11, 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.

See Also

See the pls package for further estimation algorithms.

Examples

#############################################################################
# SIMULATED EXAMPLE 1: 300 cases on 100 variables
#############################################################################
set.seed(789)

N <- 300        # number of cases
p <- 100        # number of predictors
rho1 <- .6      # correlations between predictors

# simulate data
Sigma <- diag(1-rho1,p) + rho1
X <- mvtnorm::rmvnorm( N , sigma=Sigma )
beta <- seq( 0 , 1 , len=p )
y <- ( X %*% beta )[,1] + rnorm( N , sd = .6 )
Y <- matrix(y,nrow=N , ncol=1 )

# PLS regression
res <- 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 <- matrix( yarn$density, ncol=1 )
mod2 <- kernelpls.fit2( X = yarn$NIR , Y = Y , ncomp = 10 )