I am analyzing a point pattern using G (nearest neighbor cumulative distribution) function, followed up by the envelope tests and in the same time using Nearest Neighbor Index test (Clark Evans test ). For this purpose I use spatstat, guided by "Spatial Point Patterns: Methodology and Applications with R" by Baddeley, Rubak and Turner.
To my surprise the G tests results in non rejection of the H0, while Clark Evans test suggests rejection of the H0. I was wondering if it means that one of the tests for any reason provides false result? or that due to the nature of the tests there is no fallacy in observing such results.
Another question: could Clark Evans test be used to test not only CSR, but other H0?
plot(envelope(my_pattren, Gest, correction="none"))
results of MAD test
mad.test(my_pf[], Gest) Monte Carlo test based on 99 simulations Summary function: G(r) Reference function: theoretical Alternative: two.sided Interval of distance values: [0, 77.6841825602024] Test statistic: Maximum absolute deviation Deviation = observed minus theoretical data: my_pattern mad = 0.36677, rank = 19, p-value = 0.19
Clark Evans test
clarkevans.test(my_pattern) Clark-Evans test No edge correction Z-test data: my_pattern R = 1.2298, p-value = 0.03502 alternative hypothesis: two-sided