scglr-package

SCGLR implements a new PLS regression approach in the
multivariate generalized linear framework. The method
allows the joint modeling of random variables from
different exponential family distributions, searching for
common PLS-type components. <code><a href="/link/scglr?package=SCGLR&version=1.2" rd-options="" data-mini-rdoc="SCGLR::scglr">scglr</a></code> and
<code><a href="/link/scglrCrossVal?package=SCGLR&version=1.2" rd-options="" data-mini-rdoc="SCGLR::scglrCrossVal">scglrCrossVal</a></code> are the two main functions. The
former constructs the components and performs the parameter
estimation, while the latter selects the approriate number
of components by cross-validation. Dedicated plots, print,
and summary functions are available. The package contains
also an ecological dataset dealing with the abundance of
multiple tree genera given a large number of geo-referenced
environmental variables.

SCGLR extends the Fisher Scoring Algorithm so as to combine PLS
    regression with GLM estimation in the multivariate context.

scglr-package: Supervised Component Generalized Linear Regression (SCGLR)

Description

Arguments

References