HI Cass, thanks for the reply.
I'm unsure exatly how the variable importance shown in @results relates to the training gain bar charts from the MaxEnt GUI when ticking "Do jackknife to measure variable importance".
The variable importance shown in @results is just the percentage contribution/perumtation importance- it doesn't give me any information on the absolute change in model gain when the explanatory variable is included/excluded. Further, if I run a model using dismo::maxent and check @results the list returned is much longer and gives me the absolute gain changes from the variable jackknifing process (i.e. it'll show gain with the variable and without the varaible), enabling me to plot this myself within R.
I don't think ENMEval returns this information by default because I don't think it's jackknifing the predictor variables, although I could be wrong. If it's not run by default but there is an option to jackknife the variables, do you know how to do this?
I wanted this variable jackknifing information via ENMEval if possible because I have many species to analyse. I can take the optimal model identifed by ENMeval and pass this information manually to dismo::maxent to get the information that I want but it would be a really time consuming process. Especially for the feature classes: ENMeval returns a single character (e.g. L for linear) whereas dismo::maxent expects a string (e.g. args=c("linear=TRUE")). I'd probably have to write a script to convert all the single characters into strings.
Thanks
Simon