696 Spatial Point Patterns: Methodology and Applications with R
Random effects:
(Intercept)
1 3.81e-08
2 -1.03e-07
3 6.46e-08
Random effects summary:
Random effects:
Formula:
~
1 | id
(Intercept) Residual
StdDev: 0.000214 1
Variance function:
Structure: fixed weights
Formula:
~
invwt
Interaction for all patterns: Poisson process
Each point pattern is modelled as a stationary Poisson process, but the intensity is assumed to
take differ ent values in different point patterns. Hence, this is technically a Cox process mo del (a
mixed Poisson process) rather than a Poisson proc ess. The log intensity is assumed to be normally
distributed, but this is not strictly a log-Gaussian Cox pr ocess, rather it is a degenerate case where
the driving intensity is a random con