gpls: A function to fit Generalized partial least squares models.

In Bioconductor/gpls: Classification using generalized partial least squares

A function to fit Generalized partial least squares models.

Description

Partial least squares is a commonly used dimension reduction technique. The paradigm can be extended to include generalized linear models in several different ways. The code in this function uses the extension proposed by Ding and Gentleman, 2004.

Usage

gpls(x, ...)

## Default S3 method:
gpls(x, y, K.prov=NULL, eps=1e-3, lmax=100, b.ini=NULL,
    denom.eps=1e-20, family="binomial", link=NULL, br=TRUE, ...)

## S3 method for class 'formula'
gpls(formula, data, contrasts=NULL, K.prov=NULL,
eps=1e-3, lmax=100, b.ini=NULL, denom.eps=1e-20, family="binomial",
link=NULL, br=TRUE, ...)

Arguments

x	The matrix of covariates.
formula	A formula of the form 'y ~ x1 + x2 + ...', where y is the response and the other terms are covariates.
y	The vector of responses
data	A data.frame to resolve the forumla, if used
K.prov	number of PLS components, default is the rank of X
eps	tolerance for convergence
lmax	maximum number of iteration allowed
b.ini	initial value of regression coefficients
denom.eps	small quanitity to guarantee nonzero denominator in deciding convergence
family	glm family, binomial is the only relevant one here
link	link function, logit is the only one practically implemented now
br	TRUE if Firth's bias reduction procedure is used
...	Additional arguements.
contrasts	an optional list. See the contrasts.arg of model.matrix.default.

Details

This is a different interface to the functionality provided by glpls1a. The interface is intended to be simpler to use and more consistent with other matchine learning code in R.

The technology is intended to deal with two class problems where there are more predictors than cases. If a response variable (y) is used that has more than two levels the behavior may be unusual.

Value

An object of class gpls with the following components:

coefficients	The estimated coefficients.
convergence	A boolean indicating whether convergence was achieved.
niter	The total number of iterations.
bias.reduction	A boolean indicating whether Firth's procedure was used.
family	The family argument that was passed in.
link	The link argument that was passed in.
terms	The constructed terms object.
call	The call
levs	The factor levels for prediction.

Author(s)

B. Ding and R. Gentleman

References

• Ding, B.Y. and Gentleman, R. (2003) Classification using generalized partial least squares.

• Marx, B.D (1996) Iteratively reweighted partial least squares estimation for generalized linear regression. Technometrics 38(4): 374-381.

See Also

glpls1a

Examples

library(MASS)
m1 = gpls(type~., data=Pima.tr, K=3)

Bioconductor/gpls documentation built on Nov. 2, 2024, 7:25 a.m.