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Hello 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 |
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There is currently not an explicit option for spatial analysis. You may want to have a look at the reference Liu, Wall & Hodges (2005). Generalized spatial structural equation models. Biostatistics, 6, 539-557. I don't know if Model Constraint can be used to capture any of those model features. |
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Dear Dr Muthén, Thank you for your prompt answer. 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. Thank you for your help, Eric Le Tortorec |
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Maybe you want to contact the authors of the Biostat article and ask that question. |
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