I'm using the ppm function in spatstat and looking for documentation on the default dummy points used in the quadrature scheme. The default.dummy help page says "If nd is missing, a default value (depending on the data pattern X) is computed by default.ngrid." I am now looking for information on default.ngrid but cannot find anything. For context, I am comparing null random models to models with covariates, where the points are coordinates of class ppp and the covariates are pixel images in a window. To generate the models, I'm using the ppm function with default parameters, as in:
ppm0 <- ppm(myPoints_ppp ~ 1)
ppm1 <- ppm(myPoints_ppp ~ covariate_A)
I'm trying to understand how the dummy points are being generated since I did not specify the nd argument. Thanks!
That reference is out of date. The default number of dummy points is determined by default.n.tiling. This is an internal, undocumented function.
The source code for default.n.tiling can be consulted to find out the exact rules, but here is a sketch:
Currently the default minimum number of grid points in each dimension is the greater of
10 * ceiling(2 * sqrt(npoints(X))/10)
and
spatstat.options('ndummy.min')
This determines a minimum acceptable number of dummy points. With the default value of spatstat.options('ndummy.min') = 32 this means that any point pattern with no more than 225 points will be given a minimum 32 x 32 grid of dummy points. A pattern with 226 to 400 points will have 40 x 40 dummy points. A pattern with 401 to 625 points will have 50 x 50 dummy points, and so on.
The final number of dummy points is determined by applying some other constraints (which depend on the context and user-specified parameters) and may be greater than the minimum number specified above.
This default can always be overruled by simply specifying a non-NULL value for the parameter nd.
I should also point out that the default settings are chosen to produce an acceptable result quickly, rather than to produce a highly accurate result. This is necessary because of CRAN's limits on the total time taken to check the package. If you're doing any serious methodological research, you should consider increasing spatstat.options('ndummy.min') or creating your own quadrature schemes. This will also improve the reproducibility of your results (since the default in spatstat could change.)