[R-sig-Geo] dclf.test output.

[Rolf Turner]

I gather that your problem is that you expect to reject the null 
hypothesis of "no clustering" for A vs. D and for C vs. D, but *not* to 
reject it for B vs. D.

I *think* that your problem might be the fact that you are using a 
two-sided test, which gives, roughly speaking, a test of "no 
association" rather than a test of "no clustering".  It could be the 
case that points of types B and D tend to *avoid* each other, so you get 
"significant" association between B and D, although the B points do the 
opposite of clustering around D points.

It's hard to tell for sure without a *reproducible example* (!!!).  We 
don't have access to Data.ppp.

Try using alternative="greater" in your call to dclf.test() and see if 
the results are more in keeping with your expectations.

cheers,

Rolf Turner

-- 
Technical Editor ANZJS
Department of Statistics
University of Auckland
Phone: +64-9-373-7599 ext. 88276

On 28/07/16 05:48, Guy Bayegnak wrote:

> Hi all, I have some marked spatial points and I am trying to assess
> therelative association between different types of points using the
> Diggle-Cressie-Loosmore-Ford test of CSR.
> My observations are of 4 categories (A,B,C,D) and I am trying to
> assess 3 categories (A,B,C,) against one (D), and I get the output
> provided below. Knowing the sampling area, I know category "D" and
> category "B" tend to occur all across the sampling area.
> What I am trying to prove is that category "A" and "C" tend to be
> clustered around "D". But u values I am getting are all positive, and
> the p-value are all 0.01. However, the dclf.test between A-D and C-D
> returns a u value at least 3 times as large than that of B-D.
> My question is: how do I interpret these values. Does it still show
> clustering of A and C relative to D? if yes how do I interpret the
> output of dclf.test between B and D?
> Thanks, GAB
>
>
>
> Diggle-Cressie-Loosmore-Ford test of CSR
>         Monte Carlo test based on 99 simulations
>         Summary function: Kcross["A", "D"](r)
>         Reference function: theoretical
>         Alternative: two.sided
>         Interval of distance values: [0, 1.05769125]
>         Test statistic: Integral of squared absolute deviation
>         Deviation = observed minus theoretical
>
> data:  Data.ppp
> u = 54.931, rank = 1, p-value = 0.01
>
> Diggle-Cressie-Loosmore-Ford test of CSR
>         Monte Carlo test based on 99 simulations
>         Summary function: Kcross["B", "D"](r)
>         Reference function: theoretical
>         Alternative: two.sided
>         Interval of distance values: [0, 1.05769125]
>         Test statistic: Integral of squared absolute deviation
>         Deviation = observed minus theoretical
>
> data:  Data.ppp
> u = 19.315, rank = 1, p-value = 0.01
>
> Diggle-Cressie-Loosmore-Ford test of CSR
>         Monte Carlo test based on 99 simulations
>         Summary function: Kcross["C", "D"](r)
>         Reference function: theoretical
>         Alternative: two.sided
>         Interval of distance values: [0, 1.05769125]
>         Test statistic: Integral of squared absolute deviation
>         Deviation = observed minus theoretical
>
> data:  Data.ppp
> u = 46.829, rank = 1, p-value = 0.01