I have a model from which I am attempting to create a predictive map (in GIS, but I'm aware of some options in R). The response values are constrained to [0,1] using an offset term in the model (see the full model below) and as such, raster values should also have the same constraints.
I believe the syntax in Raster Calculator is correct, but the output values aren't constrained to [0,1]. My guess at the moment is that this is because I scaled the variables around a mean of 0 and standard deviation of 1 prior to model building. Is my best bet of achieving the predictive map to also scale the rasters in GIS or R? If so, how?
Model syntax in Raster Calculator is as follows:
Ln(57064) + (-0.10872 * "var1"^2)+ (0.02844* "var2"^2)+(-0.03848 * "var3"^2)+ (0.05726*"var4") +(0.06462*"var5"^2)+(- 0.42450*"var6"^2)
Edit to clarify the process I used: I created 6 raster layers (things like distance to roads, distance to streams, etc.) in GIS from which I sampled to generate my predictor values. I standardized these predictor values using the parameterization described above. I fit a negative binomial model with these predictors, where frequency of animal use was the response. An offset term (in the syntax above, depicted as ln(total counts)) transformed frequency of use to probability of use. After some model selection, my model generated beta estimates which are standardized and therefore are best interpreted in an odds-ratio context. I'd like to use the model, which I included above, to generate a predictive map of probability of use across my study area.