gpls
A function to fit Generalized partial least squares models.
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.
 Keywords
 classif
Usage
gpls(x, ...)
"gpls"(x, y, K.prov=NULL, eps=1e3, lmax=100, b.ini=NULL, denom.eps=1e20, family="binomial", link=NULL, br=TRUE, ...)
"gpls"(formula, data, contrasts=NULL, K.prov=NULL,
eps=1e3, lmax=100, b.ini=NULL, denom.eps=1e20, 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
ofmodel.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
 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.
gpls
with the following components:
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): 374381.
See Also
Examples
library(MASS)
m1 = gpls(type~., data=Pima.tr, K=3)
Community examples
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