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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.

The kppm methods, which have the cluster models, require two-dimensional data. So, I added a dummy y-column with zero values and range of (ymin=0,ymax=0.001). 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.

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