I want to generate pseudo absences based on kernel density estimate from a linear feature spatial line layer, where pseudo absences have a higher probability of being sampled near a line.
I have tried to do this using the pseudo.absence function in the spatialEco package; however, the input pattern has to be a point pattern and I don't see how to specify a bandwidth parameter for the KDE, which I would like to do.
I tried using spatstat to create a density.psp, which I did successfully and am happy with that output, but I cannot figure out how to generate pseudo-absence points based on the KDE.
My bootleg solution was to use the spsample function in sp to create points along my line features and then use that as the point pattern input to the pseudo.absence function. This seems to work; however, I want the sample of points to be based on the inverse of the KDE output from the function.
Is there a way to do this within spatialEco?