tol.classplot function

Plot tolerance contour classification scheme

Permits illustration of the uniquely identified tolerance contour regions arising from a call to tol.classify.

tol.classplot(pcpolys, add = FALSE, annotate = TRUE, ...)

Arguments

• pcpolys: A list of polygonal windows, each of class owin. This will almost always be the pcpolys component of the object returned by a call to tol.classify.

• 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 tol.classify).

• ...: Additional arguments to be passed to text

  to control the appearance of the annotations when annotate=TRUE.

Returns

Plots to the relevant graphics device.

Details

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.

Examples

## 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)

References

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.

Author(s)

T. M. Davies