A few months back I created a spatial logistic regression model of terrestrial skunk rabies in Arizona/Colorado/New Mexico/Utah. A link to the project can be found here (https://docs.wixstatic.com/ugd/0a0a58_4a5a522e0ee944e7a186e8d1f106010d.pdf)
I'm attempting to run the model for Colorado only. Aside from only modeling for Colorado the changes to the model include:
- A higher sampling ratio. 3:1 controls/cases
- More sample points 578 positive specimens
- More years of sampling. 2008 - 2017
- More years of independent variables averaged. (e.g. monthly surface temperature averaged 2008-2017 vs 2013-2017)
- reduction in the # of rabies positive species (there are fewer species in CO positive for rabies than in the four states)
- changing from the range of species to the modeled habitat of a species based on USGS Gap View
The results for Colorado are very similar to the Colorado results in the Four Corners model. The same non-autocovariate variables seem to be significant, However the estimates as well as predicted risk are infinitesimally smaller:
I also tried standardizing my variables given the wide variety of scales. The results were the same as using non-standardized variables.
What is going on? My only hypotheses are that the slight changes in the variable definitions altered the results.
Also that the heterogeneity of the landscape in four states maybe teases out more differences than just in one state?
E.g. You don't find rabid skunks in the barren desert of Utah compared to prairie of CO, but you can find rabid skunks in the prairie steppe of Colorado and the scrubland of Western Slope.
I used Spatial Analyst for some map algebra and cell statistics functions and multi-values to points. The first go around I used ArcGIS/QGIS/R. This go-around just ArcGIS and R. – BenW 33 mins ago