I am currently doing a project on environmental determinants of malaria vector distributions. I'm using remote sensing data for environmental variables linked via GIS. I have run univariate (binomial) models of my extracted environmental variables against vector presence/absence. Each of the factors I have extracted from over four scales: at the points of the vector sampling (within household compounds), the mean over a 100m radius, 500m, and 1km.
For some variables, more than one spatial scale was significant in the univariate analyses. I want to compare these univariate models to find out which is the most influential spatial scale for that variable, and use that in my multivariate.
The AIC values I have seem to disagree with the Log likelihood values and LRT test results (if I'm doing it right that is...), and so I'm not sure how to go forward. I thought that I would need to select the model with the lowest AIC and lowest LRT test P-value I have the AIC for each model, and the Log likelihood. I hope it is correct that I have tried to compare them by taking turns to compare each univariate to a maximal model (one with all scales) with an LRT test. Here is an example.
And also an example of what I get when i run lrtest() instead:
Before doing these things, I have the odds ratios etc. for the models elsewhere. Should I select the ones with the lowest AIC, or lowest P values in the above tests?
Thanks so much!
