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Plot the result of a spatial distribution test
computed by cdf.test
.
# S3 method for cdftest
plot(x, ...,
style=c("cdf", "PP", "QQ"),
lwd=par("lwd"), col=par("col"), lty=par("lty"),
lwd0=lwd, col0=2, lty0=2,
do.legend)
Object to be plotted. An object of class "cdftest"
produced by a method for cdf.test
.
extra arguments that will be passed to the plotting function
plot.default
.
Style of plot. See Details.
The width, colour and type of lines used to plot the empirical curve (the empirical distribution, or PP plot or QQ plot).
The width, colour and type of lines used to plot the reference curve (the predicted distribution, or the diagonal).
Logical value indicating whether to add an
explanatory legend. Applies only when style="cdf"
.
NULL
.
This is the plot
method for the class "cdftest"
.
An object of this class represents the outcome of
a spatial distribution test, computed by cdf.test
,
and based on either the Kolmogorov-Smirnov,
Cramer-von Mises
or Anderson-Darling test.
If style="cdf"
(the default),
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, and the predicted
cumulative distribution function of the covariate under the model,
both plotted against the value of the covariate. The
Kolmogorov-Smirnov test statistic (for example)
is the maximum vertical separation
between the two curves.
If style="PP"
then the P-P plot is drawn. The
If style="QQ"
then the Q-Q plot is drawn. The
# NOT RUN {
op <- options(useFancyQuotes=FALSE)
# 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 <- cdf.test(fit0, xcoord)
# plot result of test
plot(k, lwd0=3)
plot(k, style="PP")
plot(k, style="QQ")
options(op)
# }
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