https://github.com/cran/spatstat
Tip revision: 7c224bf1f2e43c2c28578875568b3af111607878 authored by Adrian Baddeley on 28 November 2010, 00:00:00 UTC
version 1.21-2
version 1.21-2
Tip revision: 7c224bf
plot.bermantest.Rd
\name{plot.bermantest}
\alias{plot.bermantest}
\title{Plot Result of Berman Test}
\description{
Plot the result of Berman's test of goodness-of-fit
}
\usage{
\method{plot}{bermantest}(x, ...,
lwd=par("lwd"), col=par("col"), lty=par("lty"),
lwd0=lwd, col0=col, lty0=lty)
}
\arguments{
\item{x}{
Object to be plotted. An object of class \code{"bermantest"}
produced by \code{\link{bermantest}}.
}
\item{\dots}{
extra arguments that will be passed to the plotting function
\code{\link{plot.ecdf}}.
}
\item{col,lwd,lty}{
The width, colour and type of lines used to plot the
empirical distribution.
}
\item{col0,lwd0,lty0}{
The width, colour and type of lines used to plot the
predicted distribution.
}
}
\value{
\code{NULL}.
}
\details{
This is the \code{plot} method for the class \code{"bermantest"}.
An object of this class represents the outcome of Berman's test
of goodness-of-fit of a spatial Poisson point process model,
computed by \code{\link{bermantest}}.
For the \emph{Z1} test (i.e. if \code{x} was computed using
\code{bermantest( ,which="Z1")}),
the plot displays the two cumulative distribution functions
that are compared by the test: namely the empirical cumulative distribution
function of the covariate at the data points, \eqn{\hat F}{Fhat},
and the predicted
cumulative distribution function of the covariate under the model,
\eqn{F_0}{F0}, both plotted against the value of the covariate.
Two vertical lines show the mean values of these two distributions.
If the model is correct, the two curves should be close; the test is
based on comparing the two vertical lines.
For the \emph{Z2} test (i.e. if \code{x} was computed using
\code{bermantest( ,which="Z2")}), the plot displays the empirical
cumulative distribution function of the values
\eqn{U_i = F_0(Y_i)}{U[i] = F0(Y[i])} where \eqn{Y_i}{Y[i]} is the
value of the covariate at the \eqn{i}-th data point. The diagonal line
with equation \eqn{y=x} is also shown. Two vertical lines show the
mean of the values \eqn{U_i}{U[i]} and the value \eqn{1/2}. If the
model is correct, the two curves should be close. The test is based on
comparing the two vertical lines.
}
\seealso{
\code{\link{bermantest}}
}
\examples{
# synthetic data: nonuniform Poisson process
X <- rpoispp(function(x,y) { 100 * exp(-x) }, win=square(1))
# fit uniform Poisson process
fit0 <- ppm(X, ~1)
# test covariate = x coordinate
xcoord <- function(x,y) { x }
# test wrong model
k <- bermantest(fit0, xcoord, "Z1")
# plot result of test
plot(k, col="red", col0="green")
}
\author{Adrian Baddeley
\email{Adrian.Baddeley@csiro.au}
\url{http://www.maths.uwa.edu.au/~adrian/}
and Rolf Turner
\email{r.turner@auckland.ac.nz}
}
\keyword{spatial}
\keyword{hplot}