I appreciate for the advices, let me explain the situation a bit more with
another example;
Say, points are representing sampled fields for pests. Imagine you are making
samplings from tomato fields to reveal possible clusters (hotspots) of pests,
in a region, say 100km by 100km. Last year, you sampled 50 fields from that
region, and this year 50 fields sampled. You want to compare these two years by
means of cluster locations, but there is a problem; some tomato field locations
are same with previous year, but some are changed to (nearby fields) due to
crop-rotation, farmers' preferences etc., so these point sets do not overlap
perfectly. The question is, can you compare (and comment on) pest clusters
while point locations different among these point sets? (or, will it be
statistically correct?)
What I was hoping is, to find a way to tell, how (dis)similar the point
locations of year1 and year2 are? In other words, when compared, whether the
points of these two sampling sets are statistically significantly distributed
against each other, or not. If not, I think comparison of two point sets will
become possible. This is why I am looking for cross nearest neighor function.
Well I did my best to explain the situation, hope this clarifies the issue,
many thanks for the comments.
Alp
>
> It is not clear to me just what you are trying to do and hence it is not
> clear what (if any) the appropriate technique might be. What do the
> points actually consist of? Are the points in the June pattern translations
> (by varying amounts) of the points in the May pattern. If so, it would seem
> to me that you have a number of *trajectories* of points, observed at
> ``about 10'' (???) discrete times. In which case point pattern analysis may
> not be appropriate.
>
> Be that as it may, you could apply the Kcross() function to your data by
> combining your ``about 10'' patterns into a single pattern with marks
> indicating the month in which the pattern was observed. To combine the
> patterns, use "superimpose()". E.g. (triv. example with simulated data):
>
> require(spatstat)
> X <- list()
> set.seed(42)
> for(nm in month.abb) X[[nm]] <- rpoispp(100)
> Y <- do.call(superimpose,X)
> marks(Y) <- factor(marks(Y),levels=month.abb) # To get the months in the
> right order
> kc <-alltypes(Y,fun="K")
> plot(kc[1:4,1:4],title="Vacuous Example") # 12 x 12 makes the plots too
> small to be useful.
>
> HTH
>
> cheers,
>
> Rolf Turner
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