Continuous variable behaving as a categoric in the tMsPGOcc model #49
Description
Dear Jeff,
I have been working on fitting a dynamic model (tMsPGOcc) to a bird community dataset, aiming to explore the community's occupancy response to a categorical variable, "Type," which represents land use/land cover (farming, restoration, and forest), as well as continuous landscape variables like NDVI. However, I encountered an issue when including both the continuous variable (NDVI) and the categorical variable ("Type") in the model. Specifically, the continuous variable appears to behave as if it were categorical. The model output generates 119 parameters for NDVI—one less than the total number of sites (120). To investigate further, I ran a single-species model (tPGOcc) for one species to check if the behavior persists, and the issue remained the same. I’ve attached a screenshot below to illustrate the problem (please disregard the low Rhat and ESS values, as I used low n.samples just to test the model's operation.).
If I exclude the categorical variable "Type" and retain only the continuous variable (NDVI), the issue disappears entirely, and the model output behaves as expected. Additionally, I conducted another test by selecting only the first year of sampling and running a static multispecies model (msPGOcc). In this case, the output was correct, generating a single parameter for NDVI at the community level. These small tests suggest that the issue may be specific to the dynamic models, which seem to be misinterpreting my input data. Below, I’ve included the tMsPGOcc output using only the continuous variable for your reference.
I look forward to hearing your thoughts and guidance on this.