I am currently running some generalized least squares (gls) models with a spatial correlation structure, using the R package nlme
; they look like the following one:
bodymeanee2 <-gls(BS_mean_SCALED ~ LAT2_SCALED + LAT_SQUARED2_SCALED + SPRICHNESS_LOG_SCALED +
SPRECORDS_LOG_SCALED + SST_mean_SCALED + NPP_mean_SCALED + OCEAN, correlation =
corExp(form=~LAT_SQUARED2_SCALED+LON_SQUARED2_SCALED, nugget=TRUE), data=latitudecsv)
I am trying to extract the relative importance of each explanatory variable (predictor) by model avering, as the sum of the Akaike weights of the submodels in which the variable appeared, from the set of submodels including all combinations of explanatory variables.
I generate the complete set of submodels with all the possible combinations of explanatory variables using the function dredge
of the package MuMIn
:
ms4.1<-dredge(bodymeanee2)
I would like to know how to extract the relative importance of each explanatory variable once the function dredge
finishes. I would also like to know if it's normal that the function 'dredge' tooks hours and hours in finishing the complete set of submodels. Is there another possibility in less time?
Thank you in advance