I am developing a predictive model to apply to raster layers for land cover classification. So, I have a thematic category that is classified as agriculture but an accuracy assessment indicates commission error, savanna incorrectly classified as ag. I randomly sampled the category and have 177 observations of ag and savanna. I extracted reflectance values from 6 predictive raster layers at the observation locations. So that's my dataset. I selected 4 variables that all significantly contribute to the model. Percentage correct for selected and unselected cases are approx 90% and Nagelkerke R square is 0.880. Odds ratios for the predictors are between .599 and 1.127 but the odds ratio for the constant is very high, 564830031.5. The predictors cannot all take the value of zero. I'm not sure what to make of the odds ratio. Any insight would be greatly appreciated.