\HeaderA{confint-MASS}{Confidence Intervals for Model Parameters}{confint.Rdash.MASS}
\aliasA{confint.glm}{confint-MASS}{confint.glm}
\aliasA{confint.nls}{confint-MASS}{confint.nls}
\aliasA{confint.profile.glm}{confint-MASS}{confint.profile.glm}
\aliasA{confint.profile.nls}{confint-MASS}{confint.profile.nls}
\aliasA{profile.glm}{confint-MASS}{profile.glm}
\keyword{models}{confint-MASS}
\begin{Description}\relax
Computes confidence intervals for one or more parameters in a fitted
model.  Package \pkg{MASS} adds methods for \code{glm} and \code{nls} fits.
\end{Description}
\begin{Usage}
\begin{verbatim}
## S3 method for class 'glm':
confint(object, parm, level = 0.95, trace = FALSE, ...)

## S3 method for class 'nls':
confint(object, parm, level = 0.95, ...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{object}] a fitted model object. Methods currently exist for the classes
\code{"glm"}, \code{"nls"} and for profile objects from these classes.

\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{trace}] logical.  Should profiling be traced?

\item[\code{...}] additional argument(s) for methods.

\end{ldescription}
\end{Arguments}
\begin{Details}\relax
\code{\LinkA{confint}{confint}} is a generic function in package \code{base}.

These \code{confint} methods calls
the appropriate profile method, then finds the
confidence intervals by interpolation in the profile traces.
If the profile object is already available it should be used as the
main argument rather than the fitted model object itself.
\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{References}\relax
Venables, W. N. and Ripley, B. D. (2002)
\emph{Modern Applied Statistics with S.} Fourth edition.  Springer.
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{profile}{profile}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
expn1 <- deriv(y ~ b0 + b1 * 2^(-x/th), c("b0", "b1", "th"),
               function(b0, b1, th, x) {})

wtloss.gr <- nls(Weight ~ expn1(b0, b1, th, Days),
   data = wtloss, start = c(b0=90, b1=95, th=120))

expn2 <- deriv(~b0 + b1*((w0 - b0)/b1)^(x/d0),
         c("b0","b1","d0"), function(b0, b1, d0, x, w0) {})

wtloss.init <- function(obj, w0) {
  p <- coef(obj)
  d0 <-  - log((w0 - p["b0"])/p["b1"])/log(2) * p["th"]
  c(p[c("b0", "b1")], d0 = as.vector(d0))
}

out <- NULL
w0s <- c(110, 100, 90)
for(w0 in w0s) {
    fm <- nls(Weight ~ expn2(b0, b1, d0, Days, w0),
              wtloss, start = wtloss.init(wtloss.gr, w0))
    out <- rbind(out, c(coef(fm)["d0"], confint(fm, "d0")))
  }
dimnames(out) <- list(paste(w0s, "kg:"),  c("d0", "low", "high"))
out

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.lg0 <- glm(SF ~ sex + ldose - 1, family = binomial)
confint(budworm.lg0)
confint(budworm.lg0, "ldose")
\end{ExampleCode}
\end{Examples}

