I want to study a point pattern using cumulative NND (Nearest Neighbor Distance) "G" function . The main task is to test H0: the point process is compatible with a null model (can be CSR (complete spatial randomness) or other)
I have see two options:
Generate N point pattern under the null model. Calculate G function for all the patterns, calculate G function for the data. Run 2 sample KS test on both these G functions.
Generate N point pattern under the null model. Calculate G function for each pattern, calculate G function for the data. And use test which accounts for CE (like MAD (Maximum Absolute Deviation) or DCLF ( Diggle-Cressie-Loosmore-Ford) test)
Which way is the most appropriate (and why) for testing hypotheses based on summary functions such as distributions of NNDs?