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\name{predict.ssllrm}
\alias{predict.ssllrm}
\title{Evaluating Log-Linear Regression Model Fits}
\description{
Evaluate conditional density in a log-linear regression model fit at
arbitrary x, or contrast of log conditional density possibly with
standard errors for constructing Bayesian confidence intervals.
}
\usage{
\method{predict}{ssllrm}(object, x, y=object$qd.pt, odds=NULL, se.odds=FALSE, ...)
}
\arguments{
\item{object}{Object of class \code{"ssllrm"}.}
\item{x}{Data frame of x values.}
\item{y}{Data frame of y values; y-variables must be factors.}
\item{odds}{Optional coefficients of contrast.}
\item{se.odds}{Flag indicating if standard errors are required.
Ignored when \code{odds=NULL}.}
\item{...}{Ignored.}
}
\value{
For \code{odds=NULL}, \code{predict.ssanova} returns a vector/matrix
of the estimated \code{f(y|x)}.
When \code{odds} is given, it should match \code{y} in length and
the coefficients must add to zero; \code{predict.ssanova} then
returns a vector of estimated "odds ratios" if \code{se.odds=FALSE}
or a list consisting of the following components if
\code{se.odds=TRUE}.
\item{fit}{Vector of evaluated fit.}
\item{se.fit}{Vector of standard errors.}
}
\seealso{
Fitting function \code{\link{ssllrm}}.
}
\author{Chong Gu, \email{chong@stat.purdue.edu}}
\keyword{models}
\keyword{regression}
\keyword{smooth}
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