Why does this error occur and how to correct it?

[winklove]

nla.simp <- gbm.simplify(nla.tc5.lr005, n.drops = 5)
gbm.simplify - version 2.9
simplifying gbm.step model for BenPlankRat with 8 predictors and 38 observations
original deviance = 0.154(0.0381)
a fixed number of 5 drops will be tested
creating initial models...
Error in gbm.fit(x = x, y = y, offset = offset, distribution = distribution, :
The data set is too small or the subsampling rate is too large: nTrain * bag.fraction <= n.minobsinnode
Error: $ operator is invalid for atomic vectors

[technocrat]

See the FAQ: How to do a minimal reproducible example reprex for beginners. It's very hard to debug code without representative data. All I can say is that nla.tc5.lr005, 38 observations may be too few (the gbm.step example in the docs uses 200, but fails at 38).

library(dismo)
#> Loading required package: raster
#> Loading required package: sp
data(Anguilla_train)
# reduce data set to speed things up a bit
Anguilla_train = Anguilla_train[1:200,]
# Anguilla_train = Anguilla_train[1:38,] # fails with same error in OP
angaus.tc5.lr01 <- gbm.step(data=Anguilla_train, gbm.x = 3:14, gbm.y = 2, family = "bernoulli",
                            tree.complexity = 5, learning.rate = 0.01, bag.fraction = 0.5)
#> Loading required namespace: gbm
#> 
#>  
#>  GBM STEP - version 2.9 
#>  
#> Performing cross-validation optimisation of a boosted regression tree model 
#> for Angaus and using a family of bernoulli 
#> Using 200 observations and 12 predictors 
#> creating 10 initial models of 50 trees 
#> 
#>  folds are stratified by prevalence 
#> total mean deviance =  1.0905 
#> tolerance is fixed at  0.0011 
#> ntrees resid. dev. 
#> 50    0.9112 
#> now adding trees... 
#> 100   0.8313 
#> 150   0.7925 
#> 200   0.7704 
#> 250   0.7617 
#> 300   0.7594 
#> 350   0.763 
#> 400   0.7631 
#> 450   0.7711 
#> 500   0.7745 
#> 550   0.7776 
#> 600   0.7896 
#> 650   0.8037 
#> 700   0.819 
#> 750   0.829 
#> 800   0.8456 
#> 850   0.8561 
#> 900   0.8669 
#> 950   0.8773 
#> 1000   0.8881
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> fitting final gbm model with a fixed number of 300 trees for Angaus

#> 
#> mean total deviance = 1.09 
#> mean residual deviance = 0.415 
#>  
#> estimated cv deviance = 0.759 ; se = 0.079 
#>  
#> training data correlation = 0.855 
#> cv correlation =  0.545 ; se = 0.068 
#>  
#> training data AUC score = 0.985 
#> cv AUC score = 0.867 ; se = 0.03 
#>  
#> elapsed time -  0.03 minutes