Let me say thanks in advance.
I'm working with a set of data that contains reported coyote sightings. I use 2/3 of the data for model calibration along with an equal number of pseudo absences. I developed all possible models and ranked them according to their AIC weights. I chose the top models, who's weights summed to 75%, and created averaged model estimates for each of my parameter. Now I would like to test the accuracy of my model using the 1/3 of the data I held out. I assume I will need another equal amount of pseudo absences to include in the validation.
My problem is that I have averaged parameter estimates. All the software packages that I have come across have settings where you train your model using a specific data set and test it against a specific data set. However, I want to validate my model using averaged parameters, rather than the parameters calculated from running a simple regression.
I'm assuming I need to transform my outputs into percentages and choose an error threshold. Then I can produce a simple omission/commission report. But as this is for my Master's thesis, I want to be sure I'm using a respected and widely used method.
Can anyone point me in the right direction? My advisers are used to traditional validation methods so I'm doing a bit of outsourcing. Again, thanks for any and all advice.