Arthur Weiss wrote:
Hi everyone,
I am using the package "spatstat" for ploting kernel maps of my data.
It is a marked point pattern, the result of mosquito surveillance in a
area in a week.
For each trap, the number of individuals captured is the mark of the
point.
plot(density(X, weights=X$marks))
makes a nice kernel, but the problem is that I've got several weeks and
for each week the density is re-scaled, which makes comparisons useless.
I've been trying to find some way to pass the scale limits to the
function
but just couldn't find.
See help(plot.im) for information about how to plot a pixel image in
spatstat. The argument 'zlim' controls the range of numerical values
that are mapped to colours in the display.
If you have, say, 4 pixel images Z1, Z2, Z3, Z4 and want to plot them
all with the same colour map, try the following.
Zlist <- list(week1=Z1, week2=Z2, week3=Z3, week4=Z4)
Zrange <- range(unlist(
lapply(Zlist, function(x){summary(x)$range})))
plot(as.listof(Zlist), zlim=Zrange, ncols=2)
However, it's not clear that the command density() is really what you
want to use in this context. This command estimates the
spatially-varying average intensity (`density') of points. Are the
insect traps at fixed locations that were chosen by the experimenter? If
so, then it is somewhat meaningless to estimate their average
density.... What you need is a method for spatially interpolating the
insect counts (number of trapped insects) observed at these locations.
In spatstat you can use the command 'smooth.ppp' to perform
kernel-weighted spatial interpolation. If the insect counts are small,
then it would be more appropriate to do spatial Poisson regression
(using other packages).
Adrian Baddeley
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