\name{slice}
\alias{slice}
\alias{sliceOld}
\alias{slicetrans}
\alias{slice1D}
\alias{slice2D}
\title{Calculate likelihood "slices"}
\description{
  Computes cross-section(s) of a multi-dimensional likelihood surface
}
\usage{
slice(x, dim=1, ...)
sliceOld(fitted, which = 1:p, maxsteps = 100,
                       alpha = 0.01, zmax = sqrt(qchisq(1 - alpha/2, p)),
                       del = zmax/5, trace = FALSE,
                       tol.newmin=0.001, \dots)
slice1D(params,fun,nt=101,lower=-Inf,
                    upper=Inf,verbose=TRUE,\dots)
slice2D(params,fun,nt=31,lower=-Inf,
                    upper=Inf,
                    cutoff=10,verbose=TRUE,\dots)
slicetrans(params, params2, fun, extend=0.1, nt=401,
                       lower=-Inf, upper=Inf)
}
\arguments{
  \item{x}{a fitted model object of some sort}
  \item{dim}{dimensionality of slices (1 or 2)}
  \item{params}{a named vector of baseline parameter values}
  \item{params2}{a vector of parameter values}
  \item{fun}{an objective function}
  \item{nt}{(integer) number of slice-steps to take}
  \item{lower}{lower bound(s) (stub?)}
  \item{upper}{upper bound(s) (stub?)}
  \item{cutoff}{maximum increase in objective function to allow
    when computing ranges}
  \item{extend}{(numeric) fraction by which to extend range beyond specified points}
  \item{verbose}{print verbose output?}
  \item{fitted}{A fitted maximum likelihood model of class
    \dQuote{mle2}}
  \item{which}{a numeric or character vector describing which parameters
    to profile (default is to profile all parameters)}
  \item{maxsteps}{maximum number of steps to take looking for an upper
    value of the negative log-likelihood}
  \item{alpha}{maximum (two-sided) likelihood ratio test confidence
    level to find}
  \item{zmax}{maximum value of signed square root of deviance difference
    to find (default value corresponds to a
    2-tailed chi-squared test at level alpha)}
  \item{del}{step size for profiling}
  \item{trace}{(logical) produce tracing output?}
  \item{tol.newmin}{tolerance for diagnosing a new minimum below the
    minimum deviance estimated in initial fit is found}
  \item{\dots}{additional arguments (not used)}
}
\value{
  An object of class \code{slice} with
  \describe{
    \item{slices}{a list of individual parameter (or parameter-pair)
    slices, each of which is a data frame with elements
    \describe{
      \item{var1}{name of the first variable}
      \item{var2}{(for 2D slices) name of the second variable}
      \item{x}{parameter values}
      \item{y}{(for 2D slices) parameter values}
      \item{z}{slice values}
      \item{ranges}{a list (?) of the ranges for each parameter}
      \item{params}{vector of baseline parameter values}
      \item{dim}{1 or 2}
    }
  }
  \code{sliceOld} returns instead a list with elements \code{profile}
  and \code{summary} (see \code{\link{profile.mle2}})
}
}
\details{
  Slices provide a lighter-weight way to explore likelihood surfaces
  than profiles, since they vary a single parameter rather than
  optimizing over all but one or two parameters.

\describe{
\item{slice}{is a generic method}
\item{slice1D}{creates one-dimensional slices, by default of all
parameters of a model}
\item{slice2D}{creates two-dimensional slices, by default of all pairs
of parameters in a model}
\item{slicetrans}{creates a slice along a transect between two specified
points in parameter space (see \code{calcslice} in the \code{emdbook}
package)}
}
}
\author{Ben Bolker}
\seealso{\code{\link{profile}}}
\examples{
x <- 0:10
y <- c(26, 17, 13, 12, 20, 5, 9, 8, 5, 4, 8)
d <- data.frame(x,y)
fit1 <- mle2(y~dpois(lambda=exp(lymax)/(1+x/exp(lhalf))),
   start=list(lymax=0,lhalf=0),
   data=d)
s1 <- slice(fit1,verbose=FALSE)
s2 <- slice(fit1,dim=2,verbose=FALSE)
require(lattice)
plot(s1)
plot(s2)
## 'transect' slice, from best-fit values to another point
st <- slice(fit1,params2=c(5,0.5))
plot(st)
}
\keyword{misc}