How to figure out the parameters from mppm in R

Question

I am working using the spatstat library in R.

I have several point pattern objects built from my own dataset. The point patterns contain only the x and y coordinates of the points in them. I wanted to fit the point patterns to a Gibbs process with Strauss interaction to build a model and simulate similar point patterns. I was able to use ppm function for that purpose if I work with one point pattern at a time. I used rmhmodel function on the ppm object returned from the ppm function. The rmhmodel function gave me the parameters beta, gamma and r, which I needed to use in rStrauss function further to simulate new point patterns. FYI, I am not using the simulate function directly as I want the new simulated point pattern to have flexible number of points that simulate does not give me.

Now, if I want to work with all the point patterns I have, I can build a hyperframe of point patterns as described in the replicated point pattern chapter of the Baddeley textbook, but it requires mppm function instead of ppm function to fit the model and mppm is not working with rmhmodel when I am trying to figure out the model parameters beta, gamma and r.

How can I extract the fitted beta, gamma and r from a mppm object?

Answer 1

There are several ways to do this.

If you print a fitted model (obtained from ppm or from mppm) simply by typing the name of the object, the printed output contains a description of the fitted model including the model parameters.

If you apply the function parameters to a fitted model obtained from ppm you will obtain a list of the parameter values with appropriate names.

fit <- ppm(cells ~ 1, Strauss(0.12))
fit
parameters(fit)

For a model obtained from mppm, there could be different parameter values applying to each row of the hyperframe of data, so you would have to do lapply(subfits(model), parameters) and the result is a list with one entry for each row of the hyperframe, containing the parameters relevant to each row.

A <- hyperframe(Bugs=waterstriders)
mfit <- mppm(Bugs ~ 1, data=A, Strauss(5))
lapply(subfits(mfit), parameters)

Alternatively you can extract the canonical parameters by coef and transform them to the natural parameters.

You wrote:

I am not using the simulate function directly as I want the new simulated point pattern to have flexible number of points that simulate does not give me.

This cannot be right. The function simulate.mppm generates simulated realisations with a variable number of points. Try simulate(mfit).