\HeaderA{profile.glm}{Method for Profiling glm Objects}{profile.glm}
\keyword{regression}{profile.glm}
\keyword{models}{profile.glm}
\begin{Description}\relax
Investigates the profile log-likelihood function for a fitted model of
class \code{"glm"}.
\end{Description}
\begin{Usage}
\begin{verbatim}
## S3 method for class 'glm':
profile(fitted, which = 1:p, alpha = 0.01, maxsteps = 10,
        del = zmax/5, trace = FALSE, ...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{fitted}] the original fitted model object.
\item[\code{which}] the original model parameters which should be profiled.
This can be a numeric or character vector.
By default, all parameters are profiled.
\item[\code{alpha}] highest significance level allowed for the
profile t-statistics.
\item[\code{maxsteps}] maximum number of points to be used for profiling each
parameter.
\item[\code{del}] suggested change on the scale of the profile
t-statistics.  Default value chosen to allow profiling at about
10 parameter values.
\item[\code{trace}] logical: should the progress of profiling be reported?
\item[\code{...}] further arguments passed to or from other methods.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
The profile t-statistic is defined as the square root of change in
sum-of-squares divided by residual standard error with an
appropriate sign.
\end{Details}
\begin{Value}
A list of classes \code{"profile.glm"} and \code{"profile"} with an
element for each parameter being profiled.  The elements are
data-frames with two variables
\begin{ldescription}
\item[\code{par.vals}] a matrix of parameter values for each fitted model.
\item[\code{tau}] the profile t-statistics.
\end{ldescription}
\end{Value}
\begin{Author}\relax
Originally, D. M. Bates and W. N. Venables.  (For S in 1996.)
\end{Author}
\begin{SeeAlso}\relax
\code{\LinkA{glm}{glm}}, \code{\LinkA{profile}{profile}}, \code{\LinkA{plot.profile}{plot.profile}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
options(contrasts = c("contr.treatment", "contr.poly"))
ldose <- rep(0:5, 2)
numdead <- c(1, 4, 9, 13, 18, 20, 0, 2, 6, 10, 12, 16)
sex <- factor(rep(c("M", "F"), c(6, 6)))
SF <- cbind(numdead, numalive = 20 - numdead)
budworm.lg <- glm(SF ~ sex*ldose, family = binomial)
pr1 <- profile(budworm.lg)
plot(pr1)
pairs(pr1)
\end{ExampleCode}
\end{Examples}

