linear.pls {plsdof}R Documentation

Linear Partial Least Squares

Description

This function computes the Partial Least Squares solution and the first derivative of the regression coefficients.

Usage

linear.pls(X, y, m = ncol(X),model.selection="aic")

Arguments

X matrix of predictor observations.
y vector of response observations. The length of y is the same as the number of rows of X.
m maximal number of Partial Least Squares components. Default is m=ncol(X).
model.selection Which model selection criterion should be used? Element from c("aic","bic","gmdl").

Details

We first standardize X to zero mean and unit variance.

Value

The function returns an object of class "plsdof".

coefficients matrix of regression coefficients
intercept vector of regression intercepts
DoF Degrees of Freedom
sigmahat vector of estimated model error
dBeta array of the first derivative of coefficients
covariance array of the covariance matrices of coefficients
m.opt optimal number of PLS components, as determined by model.selection

Author(s)

Nicole Kraemer

References

Kraemer, N., Braun, M.L. (2007) "Kernelizing PLS, Degrees of Freedom, and Efficient Model Selection", Proceedings of the 24th International Conference on Machine Learning, Omni Press, 441 - 448

See Also

kernel.pls.ic, kernel.pls.cv,kernel.pls

Examples

n<-50 # number of observations
p<-5 # number of variables
X<-matrix(rnorm(n*p),ncol=p)
y<-rnorm(n)

pls.object<-linear.pls(X,y,m=5,model.selection="bic")


[Package plsdof version 0.1-1 Index]