https://github.com/cran/gss
Tip revision: 9f0152d0fb61ff50420926206dd516f6589e7a23 authored by Chong Gu on 08 August 1977, 00:00:00 UTC
version 0.8-3
version 0.8-3
Tip revision: 9f0152d
summary.gssanova1.Rd
\name{summary.gssanova1}
\alias{summary.gssanova1}
\title{Assessing Smoothing Spline ANOVA Fits with Non Gaussian Responses}
\description{
Calculate various summaries of smoothing spline ANOVA fits with non
Gaussian responses.
}
\usage{
summary.gssanova1(object, diagnostics=FALSE, ...)
}
\arguments{
\item{object}{Object of class \code{"gssanova1"}.}
\item{diagnostics}{Flag indicating if diagnostics are required.}
\item{...}{Ignored.}
}
\value{
\code{summary.gssanova1} returns a list object of \code{\link{class}}
\code{"summary.gssanova1"} consisting of the following components.
The entries \code{pi}, \code{kappa}, \code{cosines}, and
\code{roughness} are only calculated if \code{diagnostics=TRUE}.
\item{call}{Fitting call.}
\item{family}{Error distribution.}
\item{alpha}{Parameter used to define cross-validation in model
fitting.}
\item{fitted}{Fitted values on the scale of the link.}
\item{dispersion}{Assumed or estimated dispersion parameter.}
\item{residuals}{Working residuals on the link scale.}
\item{rss}{Residual sum of squares.}
\item{dev.resid}{Deviance residuals.}
\item{deviance}{Deviance of the fit.}
\item{dev.null}{Deviance of the null model.}
\item{penalty}{Roughness penalty associated with the fit.}
\item{pi}{"Percentage decomposition" of "explained variance" into
model terms.}
\item{kappa}{Concurvity diagnostics for model terms. Virtually the
square roots of variance inflation factors of a retrospective
linear model.}
\item{cosines}{Cosine diagnostics for practical significance of
model terms.}
\item{roughness}{Percentage decomposition of the roughness penalty
\code{penalty} into model terms.}
}
\details{
Similar to the iterated weighted least squares fitting of
\code{\link{glm}}, penalized likelihood regression fit can be calculated
through iterated penalized weighted least squares.
The diagnostics are based on the "pseudo" Gaussian response model
behind the weighted least squares problem at convergence.
}
\author{Chong Gu, \email{chong@stat.purdue.edu}}
\seealso{
Fitting function \code{\link{gssanova1}} and methods
\code{\link{predict.ssanova}}, \code{\link{fitted.gssanova}}.
}
\keyword{models}
\keyword{regression}
\keyword{smooth}