| plot.palmdiag {spatstat.model} | R Documentation |
Plot the Palm Intensity Diagnostic
Description
Plot the Palm intensity diagnostic for a fitted cluster process or Cox process model.
Usage
## S3 method for class 'palmdiag'
plot(x, ...,
style = c("intervals", "dots", "bands"),
args.dots = list(pch = 16), args.intervals = list(),
xlim=NULL, main)
Arguments
x |
Object of class |
... |
Additional arguments passed to |
style |
Character string specifying the style of plot for the nonparametric estimates. See Details. |
args.dots |
Arguments passed to |
args.intervals |
Arguments passed to |
xlim |
Optional range of distances plotted along the horizontal axis. A numeric vector of length 2. |
main |
Optional main title for plot. |
Details
This function plots the diagnostic proposed by
Tanaka, Ogata and Stoyan (2008, Section 2.3)
for assessing goodness-of-fit of a Neyman-Scott cluster process model
to a point pattern dataset. The diagnostic is computed by
the function palmdiagnose.
First the Palm intensity of the fitted model is plotted as a
function of interpoint distance r using
plot.fv. Then the nonparametric estimates
of the Palm intensity are plotted on the same graph as follows:
if
style="dots", the nonparametric estimate for each band of distances is plotted as a dot, with horizontal coordinate at the middle of the band. This is the style proposed by Tanaka et al (2008).if
style="intervals"(the default), each nonparametric estimate is plotted as a dot, and a 95% confidence interval is plotted as a vertical line segment, centred on the dot. The confidence interval is based on the Poisson approximation.if
style="bands", the nonparametric estimates are drawn as a continuous curve which is flat on each band of distances. The 95% confidence intervals are drawn as grey shading.
Value
Null.
Author(s)
Adrian Baddeley Adrian.Baddeley@curtin.edu.au.
References
Tanaka, U., Ogata, Y. and Stoyan, D. (2008) Parameter estimation and model selection for Neyman-Scott Point Processes. Biometrical Journal 50, 1, 43–57.
See Also
Examples
fit <- kppm(redwood)
R <- palmdiagnose(fit)
plot(R, style="d")
plot(R)
plot(R, style="b")