Classifies observed case/control points according to an estimated p-value surface.
tol.classify(rs, cutoff = 0.05, pim = NULL, ...)A list of ten components:
Point pattern of 'case' observations classified as being inside
the cutoff contours of the p-value surface. An object of class
ppp.
Point pattern of 'case' observations falling outside the
cutoff contours of the p-value surface. An object of class
ppp.
As fin, for the control points.
As fout, for the control points.
Numeric vector giving the raw index values of the original
pattern of cases which provide fin.
As findex, for the controls.
A list of the indexes in findex, with separate members
splitting up the indexes of case observations as falling inside each unique
tolerance contour.
As ginsplit, for the controls.
The classification object of class owin. This is
a pixel image mask derived from pim and cutoff.
A list of the same length as finsplit and ginsplit,
identifying each unique contour as a polygonal owin. The order of these
objects in the list correspond to the membership of finsplit and ginsplit. Use
tol.classplot on this component to plot the classification indexes.
An object of class rrs giving the estimated relative
risk function of the case-control points to be classified.
A numeric value between 0 and 1, defining the cutoff p-value used to classify points; defaults to 0.05.
A pixel image defining the p-value
surface with respect to which the observations are to be classified.
Typically the result of a call to tolerance. Ignored if
rs possesses a non-NULL P component (which takes
precedence).
Arguments to be passed to tolerance in order to
compute the desired p-value surface in the event neither rs$P nor
pim exist. Ignored otherwise.
T. M. Davies
This function takes in a relative risk surface computed with
risk and corresponding p-value surface (the latter used for
drawing tolerance contours), and attempts to classify both the case and
control points as either falling within or without contours drawn at a level
of cutoff. Points that fall 'inside' the contours are deemed to be
associated with p-values less than or equal to cutoff and hence are
usually interpreted as being in spatial areas of significant risk. This is
useful for identifying characteristics of points that fall inside
'pockets of significance' as delineated by tolerance contours.
Upon execution, the function first inspects the rs object to
determine whether it possesses a P component (i.e. an internally
computed p-value surface provided when risk is called with
optional argument tolerate=TRUE). If it exists, this is used. If not,
the function then looks to see if the pim argument has been supplied.
If it has, it must be a pixel image compatible
with the risk surface in rs$rr. If neither rs$P nor pim
is present, the function internally calls tolerance with
arguments supplied to ... to produce the desired surface.
The return object is a list that splits each of the case and control ppp
data objects (these are stored as rs$f$pp and rs$g$pp) in the
originally supplied risk surface object) into two constituent ppp
objects -- one comprising the points inside the cutoff contours (fin and gin), the
other for those points outside the cutoff contours (fout and gout).
In addition, the index values of the original data objects rs$f$pp and
rs$g$pp that correspond to the points in fin and gin are
provided as numeric vectors (findex and gindex). These objects
are useful if you need to cross-reference data-specific characteristics from
some other (corresponding) data set.
Further supplied in the returned list are quantities describing the overall classification
structure (pcmask), as well as contour-specific identification and classification
(finsplit, ginsplit, pcpolys). The pcpolys object can be plotted
to illustrate the unique contour IDs with tol.classplot.
Davies, T.M. and Hazelton, M.L. (2010), Adaptive kernel estimation of spatial relative risk, Statistics in Medicine, 29(23) 2423-2437.
Hazelton, M.L. and Davies, T.M. (2009), Inference based on kernel estimates of the relative risk function in geographical epidemiology, Biometrical Journal, 51(1), 98-109.
Kelsall, J.E. and Diggle, P.J. (1995), Kernel estimation of relative risk, Bernoulli, 1, 3-16.
data(pbc)
pbccas <- split(pbc)$case
pbccon <- split(pbc)$control
h0 <- OS(pbc,nstar="geometric")
pbcrr <- risk(pbccas,pbccon,h0=h0,tolerate=TRUE)
pbcclass <- tol.classify(pbcrr)
if (FALSE) {
plot(pbcrr)
points(pbcclass$fin,col="red",pch=3,cex=0.5)
points(pbcclass$fout,col="seagreen4",cex=0.5)
chrr <- risk(chorley,h0=0.7,tolerate=TRUE)
chclass <- tol.classify(chrr,cutoff=0.4)
plot(chrr,tol.args=list(levels=0.4))
for(i in 1:length(chclass$finsplit)){
points(chrr$f$pp[chclass$finsplit[[i]]],col=i,pch=19)
}
}
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