y = cDat$pH60_100cm[trainingREP],
cubistControl(rules = 5,
extrapolation = 5), committees = 3)
It is possible to get the variable importance (percentages in variable
usage in the models) after running the models. Because of the random
sampling at each run of the model, the variable importance is different. A
robust estimate may be determined by taking the average of all the
percentages of usage for each specific variable involved in the models.
However using varImp(fit_cubist) only gives variable importance for the
Is there anyway to extract the variable importance for each model and
arrange them finally to get the final table as presented below? I am
actually running the models 100 times. It is possible to do it manually by
saving to file each model and then calls in each model but the workload is
too high when you have 100 number of bootstraps.
The final table I am expecting should like the table below.