plsdof (version 0.2-1)

kernel.pls.fit: 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).

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

Details

We first standardize X to zero mean and unit variance.

References

Kraemer, N., Sugiyama M. (2010). "The Degrees of Freedom of Partial Least Squares Regression". preprint, http://arxiv.org/abs/1002.4112 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

linear.pls.fit, pls.cv,pls.model, pls.ic

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)