gbm.step unable to fit some models

[ggrittz]

Hello,

I've been using dismo gbm function gbm.step with the following hyperparameters:

brt <- gbm.step(data = train,
                          gbm.x = 5:9, # column indices for covariates
                          gbm.y = 4, # column index for response (presence-absence)
                          family = "bernoulli",
                          tree.complexity = ifelse(prNum < 50, 1, 5),
                          learning.rate = 0.001,
                          bag.fraction = 0.75,
                          max.trees = 10000,
                          n.trees = 50,
                          n.folds = 5, # 5-fold cross-validation
                          silent = TRUE) # avoid printing the cv results

The issue that I am encountering is related (I think) to the max.trees parameter or n.trees parameter. Species with low prevalence (20~30 presences out of 449 absences) return the following error:

Error in UseMethod("predict") :
no applicable method for 'predict' applied to an object of class "NULL"
In addition: There were 50 or more warnings (use warnings() to see the first 50)

The model brt becomes NULL and my loop stops because of that. I tried to use ifelse for those species, changing the number of max.trees to 50, and it worked for some of them, but not all. Any idea about what is happening here? I haven't found anything like this on the internet. If needed, I can upload my script and rds files. Thanks!

[ggrittz]

Oh, another weird thing is that even if I am using ifelse, the models for those low prevalence species are generated with much more than 50 max.trees. But, if I don't use ifelse, the error returns.