dclf.test output.

Thanks for your response Rolf,
You summarized it correctly.  However, B and D do not necessarily avoid each other. They could and do in fact occur next to each other at times just by coincidence, simply because both categories tend to occur all over the place, while I think A and C are influenced by D. I included the alternative="greater" but I still get the same results.
A sample of my data is provided below( I have more than 800 points).

Longitude        Latitude               Type
1 -113.1923      51.02913       C
2 -113.2013      52.83306       A
3 -113.6834     51.06585        A
4 -113.0295      50.97140       C
5 -113.2366      50.96440       A
6 -113.5849      51.37568       A
7 -113.6877      51.09027       D
8 -113.5371      51.82780       D

 I used the following code and got the results provided earlier:


dclf.test(Data.ppp,Kcross, i = "A", j = "D", alternative="greater" ,correction = "border")
dclf.test(Data.ppp,Kcross, i = "B", j = "D", alternative="greater" ,correction = "border")
dclf.test(Data.ppp,Kcross, i = "C", j = "D", alternative="greater" ,correction = "border")




Thanks,
GAB

-----Original Message-----
From: Rolf Turner [mailto:r.turner at auckland.ac.nz]
Sent: Wednesday, July 27, 2016 3:48 PM
To: Guy Bayegnak
Cc: r-sig-geo at r-project.org
Subject: Re: [R-sig-Geo] dclf.test output.


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

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

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