View source: R/tol.classplot.R
tol.classplot | R Documentation |
Permits illustration of the uniquely identified tolerance contour regions
arising from a call to tol.classify
.
tol.classplot(pcpolys, add = FALSE, annotate = TRUE, ...)
pcpolys |
A list of polygonal windows, each of class |
add |
A logical value indicating whether to add the unique regions to an existing plot (see 'Examples'). |
annotate |
A logical value indicating whether to annotate each unique
region with its identifying number (which will correspond to the uniquely
split/classified points in a corresponding call to |
... |
Additional arguments to be passed to |
The tol.classify
function permits identification of
individual significance regions (that is, the tolerance contours). In
turn, tol.classplot
may be used to visualise these regions
optionally annotated by their unique identification number to better
understand the region-specific classifications of the case and control points.
Plots to the relevant graphics device.
T. M. Davies
Davies, T.M. and Hazelton, M.L. (2010), Adaptive kernel estimation of spatial relative risk, Statistics in Medicine, 29(23) 2423-2437.
Hazelton, M.L. and Davies, T.M. (2009), Inference based on kernel estimates of the relative risk function in geographical epidemiology, Biometrical Journal, 51(1), 98-109.
Kelsall, J.E. and Diggle, P.J. (1995), Kernel estimation of relative risk, Bernoulli, 1, 3-16.
## Not run:
chrr <- risk(chorley,h0=0.7,tolerate=TRUE)
chclass <- tol.classify(chrr,cutoff=0.4)
oldpar <- par(mfrow=c(1,3))
#
plot(chrr,tol.args=list(levels=0.4))
tol.classplot(chclass$pcpolys)
plot(Window(chorley))
axis(1)
axis(2)
box(bty="l")
tol.classplot(chclass$pcpolys,add=TRUE,col=2,font=2,cex=1.5)
#
par(oldpar)
## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.