R: Kernel Partial Least Squares Fit

kernel.pls.fit {plsdof}

R Documentation

Kernel Partial Least Squares Fit

Description

This function computes the Partial Least Squares fit. This algorithm scales mainly in the number of observations.

Usage

kernel.pls.fit(X, y, m, compute.jacobian,DoF.max)

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).
`compute.jacobian`	Should the first derivative of the regression coefficients be computed as well? Default is `FALSE`

DoF.max

upper bound on the Degrees of Freedom. Default is min(ncol(X)+1,nrow(X)-1).

Details

We first standardize X to zero mean and unit variance.

Value

`coefficients`	matrix of regression coefficients
`intercept`	vector of regression intercepts
`DoF`	Degrees of Freedom
`sigmahat`	vector of estimated model error
`Yhat`	matrix of fitted values
`yhat`	vector of squared length of fitted values
`RSS`	vector of residual sum of error
`covariance`	`NULL` object.
`TT`	matrix of normalized PLS components

Author(s)

Nicole Kraemer, Mikio L. Braun

References

Kraemer, N., Sugiyama M. (2011). "The Degrees of Freedom of Partial Least Squares Regression". Journal of the American Statistical Association 106 (494) http://pubs.amstat.org/doi/abs/10.1198/jasa.2011.tm10107

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

Examples

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


pls.object<-kernel.pls.fit(X,y,m=5,compute.jacobian=TRUE)

[Package plsdof version 0.2-7 Index]