The function takes an inital cross-validated model as produced by gbm.step and then assesses the potential to remove predictors using k-fold cross validation. This done for each fold, removing the lowest contributing predictor, and repeating this process for a set number of steps. After the removal of each predictor, the change in predictive deviance is computed relative to that obtained when using all predictors. The function returns a list containing the mean change in deviance and its standard error as a function of the number of variables removed. Having completed the cross validation, it then identifies the sequence of variable to remove when using the full data set, testing this up to the number of steps used in the cross-validation phase of the analysis with results reported to the screen.
The function returns a table containing the order in which variables are to be removed and some vectors, each of which specifies the predictor column numbers in the original dataframe - the latter can be used as an argument to gbm.step e.g., gbm.step(data = data, gbm.x = simplify.object$pred.list[[4]]... would implement a new analysis with the original predictor set, minus its four lowest contributing predictors.
gbm.simplify(gbm.object, n.folds = 10, n.drops = "auto", alpha = 1, prev.stratify = TRUE,
eval.data = NULL, plot = TRUE)A list with these elements: deviance.summary, deviance.matrix, drop.count, final.drops, pred.list, and gbm.call = gbm.call))
a gbm object describing sample intensity
number of times to repeat the analysis
can be automatic or an integer specifying the number of drops to check
controls stopping when n.drops = "auto"
use prevalence stratification in selecting evaluation data
an independent evaluation data set - leave here for now
plot results
John R. Leathwick and Jane Elith
Elith, J., J.R. Leathwick and T. Hastie, 2009. A working guide to boosted regression trees. Journal of Animal Ecology 77: 802-81