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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