I have data of one-dimensional points (locations in a straight line). I want to check if the cluster poisson process models or Cox models are a good fit for the data.
Since my data only has x-coordinate, I tried the linear network poisson process model. However,
lppm supports only Poisson models.
kppm methods, which have the cluster models, require two-dimensional data. So, I added a dummy y-column with
zero values and range of
ymax=0 returns errors during computation. Now, I am able to fit Matern cluster, etc.
My question is what would be the best way to handle one-dimensional data?
Is adding a dummy column with a non-zero range the only solution? Or did I miss some details about point patterns or process models?
Suggestions for alternatives are also welcome.