I would like to fit a single model to several independent datasets in R using the spatstat package. Here, I have 3 independent datasets (ppp objects called NMJ1, NMJ2, and NMJ3), to which I want to fit a common model. The way to go should be to use the mppm function:
data <- listof(NMJ1,NMJ2,NMJ3)
data <- hyperframe(X=1:3, Points=data)
r <- matrix(c(120, 240, 240, 90), nrow = 2, ncol = 2)
model <- mppm(Points ~marks*abs(sqrt(x^2+y^2)), data, MultiStrauss(r))
In the model I am fitting, the intensity is a function of the distance to the center of the window and I supposed a MultiStrauss interaction scheme.
However, the mppm function is going to fit each dataset independently. When typing subfits(model), the fitted trend coefficients are the same for each dataset, but not the gamma coefficients. Similarly, in plotting the results of simulate(model), I observe significant and consistent differences between the 3 plots.
What is the best way to handle independent datasets (repetition of samples from the same model) in spatstat ?
This is a bug.
Your code is correct for this purpose. (Namely, when we want the same interaction coefficients to apply to all of the point patterns.)
There is a bug in the function subfits in the package spatstat.core.
The fitted model returned by mppm is correct, but the list of sub-models returned by subfits is partially incorrect.
The bug will be fixed shortly, in the development version spatstat.core 2.3-0.011 available from the GitHub repository