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I am using the glmmTMB package in R to run a logistic regression glm model with fixed and random effects (random intercepts and slopes). For some background, I have 5 fixed covariates, one of which includes a quadratic (so really 6 fixed effects) and I am including random slopes for each of those 6 covariates. Prior to running my model, I have scaled and centered each covariate (using the scale function) and checked for correlation between covariates (other than the quadratic, correlation < 0.6). I would like to convert the estimates from the model (which are standardized) to unstandardized estimates because I need to create a predictive map in ArcGIS, which have unstandardized rasters. For obtaining unstandardized estimates for use in ArcGIS, I have tried running my model with the raw data (i.e. skipping the scale and center code) but I believe I am running into convergence issues because even though it runs without warnings, the estimates have large standard errors (10-100x larger than the estimate) and the relationship of the estimate (+ or -) flips between the standardized and unstandardized runs. I have found similar posts such as this, this, and this but I don't think they are exactly my issue, or I am not understanding the math in the solutions. Advice would be very much appreciated.

bruss
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  • What do you want to convert? Coefficients? Do you need to convert the random-effects components as well or just the fixed-effect components (i.e., population-level predictions rather than group-level predictions)? In a model *with interactions*, centering can definitely change the coefficients. Can you show us the results with/without centering & scaling? Have you tried any of the solutions in the other posts/can you be more specific about what doesn't apply? – Ben Bolker May 22 '22 at 19:30
  • can you provide some example data and code? And for predicting to raster cells it would probably be much easier to use terra::predict (and why not standardize the predictor rasters?). – Robert Hijmans May 22 '22 at 21:53

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