\HeaderA{dose.p}{Predict Doses for Binomial Assay model}{dose.p}
\aliasA{print.glm.dose}{dose.p}{print.glm.dose}
\keyword{regression}{dose.p}
\keyword{models}{dose.p}
\begin{Description}\relax
Calibrate binomial assays, generalizing the calculation of LD50.
\end{Description}
\begin{Usage}
\begin{verbatim}
dose.p(obj, cf = 1:2, p = 0.5)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{obj}] A fitted model object of class inheriting from \code{"glm"}.

\item[\code{cf}] The terms in the coefficient vector giving the intercept and
coefficient of (log-)dose

\item[\code{p}] Probabilities at which to predict the dose needed.

\end{ldescription}
\end{Arguments}
\begin{Value}
An object of class \code{"glm.dose"} giving the prediction (attribute
\code{"p"} and standard error (attribute \code{"SE"}) at each response
probability.
\end{Value}
\begin{References}\relax
Venables, W. N. and Ripley, B. D. (2002)
\emph{Modern Applied Statistics with S.}
Springer.
\end{References}
\begin{Examples}
\begin{ExampleCode}
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)

dose.p(budworm.lg0, cf = c(1,3), p = 1:3/4)
dose.p(update(budworm.lg0, family = binomial(link=probit)),
       cf = c(1,3), p = 1:3/4)
\end{ExampleCode}
\end{Examples}

