\HeaderA{confint}{Confidence Intervals for Model Parameters}{confint}
\methaliasA{confint.default}{confint}{confint.default}
\keyword{models}{confint}
\begin{Description}\relax
Computes confidence intervals for one or more parameters in a fitted
model.  There is a default and a method for objects inheriting from class
\code{"\LinkA{lm}{lm}"}.
\end{Description}
\begin{Usage}
\begin{verbatim}
confint(object, parm, level = 0.95, ...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{object}] a fitted model object.
\item[\code{parm}] a specification of which parameters are to be given
confidence intervals, either a vector of numbers or a vector of
names.  If missing, all parameters are considered.
\item[\code{level}] the confidence level required.
\item[\code{...}] additional argument(s) for methods.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
\code{confint} is a generic function.  The default method assumes
asymptotic normality, and needs suitable \code{\LinkA{coef}{coef}} and
\code{\LinkA{vcov}{vcov}} methods to be available.  The default method can be
called directly for comparison with other methods.

For objects of class \code{"lm"} the direct formulae based on \eqn{t}{}
values are used.

There are stub methods for classes \code{"glm"} and \code{"nls"} which
invoke those in package \pkg{MASS} which are based on profile
likelihoods.
\end{Details}
\begin{Value}
A matrix (or vector) with columns giving lower and upper confidence
limits for each parameter. These will be labelled as (1-level)/2 and
1 - (1-level)/2 in \% (by default 2.5\% and 97.5\%).
\end{Value}
\begin{SeeAlso}\relax
\code{\LinkA{confint.glm}{confint.glm}} and \code{\LinkA{confint.nls}{confint.nls}} in
package \pkg{MASS}.
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
fit <- lm(100/mpg ~ disp + hp + wt + am, data=mtcars)
confint(fit)
confint(fit, "wt")

## from example(glm) (needs MASS to be present on the system)
counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3,1,9); treatment <- gl(3,3)
glm.D93 <- glm(counts ~ outcome + treatment, family=poisson())
confint(glm.D93)
confint.default(glm.D93)  # based on asympotic normality
\end{ExampleCode}
\end{Examples}

