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

[Guy Bayegnak]

Hi all,
I have some marked spatial points and I am trying to assess the relative 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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