I am conducting a SEM to analyse how the landscape structure at different scales (200-600m) affects the breeding success of the Eurasian treecreeper. For this analysis I am using an eight year dataset of breeding data collected from a set of about 200 nest boxes. A variogram analysis of the model residuals (obtained through SAS) showed that my response variable (number of fledged chicks) displays spatial autocorrelation. My question is: is there any way to take spatial autocorrelation into account in MPLUS?
Thank you very much for your help, Eric Le Tortorec
As a follow up to my question, would it make sense to attempt to control for the spatial autocorrelation in the response variables before using them in the SEM? For example, I have considered using the residuals from an intercept-only glmm, which accounts for spatial autocorrelation in its variance-covariance structure, as the response variable.