I have a spatial data set with fire severities (ordinal response). I include different predictor variables such as topography, vegetation cover etc. to predict fire severity using ordinal logistic regression. To correct the biased variances due to the high autocorrelation, I applied a robust covariance estimator (function robcov in the Design package in R). This covariance estimator takes into account intra-cluster correlation, i.e. autocorrelation within each patch of fire severity.
This means that I have correctly estimated regression coefficients that I can use for prediction, and the variances are corrected that allow valid inference. However, is there still a model missspecification since the problem with the autocorrelation of the residuals remains? How shall I deal with this issue?
Thanks for your help! Christof
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