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  \code{\link{plot}} and \code{\link{pairs}} methods for objects of
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  \code{\link{plot}} and \code{\link{pairs}} methods for objects of
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  class \code{"profile"}.
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  class \code{"profile"}.
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}
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}
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\usage{
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\usage{
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\method{plot}{profile}(x, ...)
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\method{plot}{profile}(x, ...)
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\method{pairs}{profile}(x, colours = 2:3, ...)
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\method{pairs}{profile}(x, colours = 2:3, which = names(x), ...)
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}
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}
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\arguments{
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\arguments{
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  \item{x}{an object inheriting from class \code{"profile"}.}
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  \item{x}{an object inheriting from class \code{"profile"}.}
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  \item{colours}{Colours to be used for the mean curves conditional on
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  \item{colours}{colours to be used for the mean curves conditional on
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    \code{x} and \code{y} respectively.}
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    \code{x} and \code{y} respectively.}
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  \item{which}{names or number of parameters in pairs plot}
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  \item{\dots}{arguments passed to or from other methods.}
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  \item{\dots}{arguments passed to or from other methods.}
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}
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}
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\details{
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\details{
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  This is the main \code{plot} method for objects created by
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  This is the main \code{plot} method for objects created by
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  \code{\link{profile.glm}}.  It can also be called on objects created
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  \code{\link{profile.glm}}.  It can also be called on objects created
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  give the loci of the points at which the tangents to the contours of
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  give the loci of the points at which the tangents to the contours of
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  the bivariate profile likelihood become vertical and horizontal,
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  the bivariate profile likelihood become vertical and horizontal,
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  respectively.  In the case of an exactly bivariate normal profile
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  respectively.  In the case of an exactly bivariate normal profile
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  likelihood, these two curves would be straight lines giving the
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  likelihood, these two curves would be straight lines giving the
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  conditional means of y|x and x|y, and the contours would be exactly
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  conditional means of y|x and x|y, and the contours would be exactly
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  elliptical. The \code{which} argument allows you to select a subset
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  of parameters; the default corresponds to the set of parameters that have
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  elliptical.
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  been profiled.
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}
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}
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\author{
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\author{
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  Originally, D. M. Bates and W. N. Venables for S (in 1996).
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  Originally, D. M. Bates and W. N. Venables for S (in 1996).
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  Taken from \pkg{MASS} where these functions were re-written by
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  Taken from \pkg{MASS} where these functions were re-written by
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  B. D. Ripley for \R (by 1998).
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  B. D. Ripley for \R (by 1998).
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}
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}
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\seealso{
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\seealso{
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  \code{\link{profile.glm}}, \code{\link{profile.nls}}.
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  \code{\link{profile.glm}}, \code{\link{profile.nls}}.
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}
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}
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\examples{
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\examples{
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## see ?profile.glm for an example using glm fits.
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## see ?profile.glm for another example using glm fits.
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## a version of example(profile.nls) from R >= 2.8.0
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## a version of example(profile.nls) from R >= 2.8.0
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fm1 <- nls(demand ~ SSasympOrig(Time, A, lrc), data = BOD)
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fm1 <- nls(demand ~ SSasympOrig(Time, A, lrc), data = BOD)
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pr1 <- profile(fm1, alphamax = 0.1)
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pr1 <- profile(fm1, alphamax = 0.1)
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stats:::plot.profile(pr1) ## override dispatch to plot.profile.nls
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stats:::plot.profile(pr1) ## override dispatch to plot.profile.nls
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## an example from ?nls
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## an example from ?nls
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x <- -(1:100)/10
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x <- -(1:100)/10
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y <- 100 + 10 * exp(x / 2) + rnorm(x)/10
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y <- 100 + 10 * exp(x / 2) + rnorm(x)/10
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nlmod <- nls(y ~  Const + A * exp(B * x), start=list(Const=100, A=10, B=1))
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nlmod <- nls(y ~  Const + A * exp(B * x), start=list(Const=100, A=10, B=1))
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pairs(profile(nlmod))
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pairs(profile(nlmod))
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## example from Dobson (1990) (see ?glm)
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counts <- c(18,17,15,20,10,20,25,13,12)
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outcome <- gl(3,1,9)
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treatment <- gl(3,3)
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## this example is only formally a Poisson model. It is really a
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## comparison of 3 multinomials. Only the interaction parameters are of
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## interest.
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glm.D93i <- glm(counts ~ outcome * treatment, family = poisson())
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pr1 <- profile(glm.D93i)
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pr2 <- profile(glm.D93i, which=6:9)
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plot(pr1)
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plot(pr2)
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pairs(pr1)
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pairs(pr2)
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}
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}
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\keyword{models}
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\keyword{models}
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\keyword{hplot}
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\keyword{hplot}