Sure, it would look something like this, assuming "e" is your ENMevaluation object, "x" is your independent dataset, "bg" is your background points, and "z" is your RasterStack of predictor variables.
# number of models total
n <- length(e@models)
# empty vector for aucs
aucs <- numeric(n)
# extract the raster values for each dataset
x.vals <- extract(z, x)
bg.vals <- extract(z, bg)
for(i in n) {
# model at index i
m <- e@models
[[i]]
# run evaluation (calculate confusion matrix)
v <- evaluate(x.vals, bg.vals, m)
# load the resulting AUC into the vector
aucs[i] <- v@auc
}
I haven't tested it, so it may require a little tweaking, but it should work. Also, you can pull out any slot in the object that comes out of evaluate() to record in your vector. Hope this helps.