Description Usage Arguments Details Value Author(s) References See Also Examples
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.
1 2 3 4 5 6 7 8 9 10  gpls(x, ...)
## Default S3 method:
gpls(x, y, K.prov=NULL, eps=1e3, lmax=100, b.ini=NULL,
denom.eps=1e20, family="binomial", link=NULL, br=TRUE, ...)
## S3 method for class 'formula'
gpls(formula, data, contrasts=NULL, K.prov=NULL,
eps=1e3, lmax=100, b.ini=NULL, denom.eps=1e20, family="binomial",
link=NULL, br=TRUE, ...)

x 
The matrix of covariates. 
formula 
A formula of the form 'y ~ x1 + x2 + ...', where

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, 
link 
link function, 
br 
TRUE if Firth's bias reduction procedure is used 
... 
Additional arguements. 
contrasts 
an optional list. See the 
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.
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 
link 
The 
terms 
The constructed terms object. 
call 
The call 
levs 
The factor levels for prediction. 
B. Ding and R. Gentleman
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.
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