gbm_tmp <- dismo::gbm.step(data = data,
                               gbm.x = predictor_names ,
                               gbm.y = i,
                               #site.weights = weights,
                               max.trees = 10000,
                               var.monotone = mon_vec,
                               family = "gaussian", learning.rate = 0.0005,
                               bag.fraction = 0.7, n.folds = 10,      #n.trees=1100,
                               tree.complexity = 9,          prev.stratify = F,
                               step.size = 25                                   )