Caret error using GBM, but not without caret

Question

I've been using gbm through caret without problems, but when removing some variables from my dataframe it started to fail. I've tried with both github and cran versions of the mentioned packages.

This is the error:

> fitRF = train(my_data[trainIndex,vars_for_clust], clusterAssignment[trainIndex], method = "gbm", verbose=T)
Something is wrong; all the Accuracy metric values are missing:
    Accuracy       Kappa    
 Min.   : NA   Min.   : NA  
 1st Qu.: NA   1st Qu.: NA  
 Median : NA   Median : NA  
 Mean   :NaN   Mean   :NaN  
 3rd Qu.: NA   3rd Qu.: NA  
 Max.   : NA   Max.   : NA  
 NA's   :9     NA's   :9    
Error in train.default(my_data[trainIndex, vars_for_clust], clusterAssignment[trainIndex],  : 
  Stopping
In addition: There were 50 or more warnings (use warnings() to see the first 50)
> warnings()
Warning messages:
1: In eval(expr, envir, enclos) :
  model fit failed for Resample01: shrinkage=0.1, interaction.depth=1, n.minobsinnode=10, n.trees=150 Error in gbm.fit(x = structure(list(relatedness_cottle = c(0, 0, 8, 6,  : 
  unused arguments (x = list(relatedness_cottle = c(0, 0, 8, 6, 0, 6, 8, 10, 10, 6, 6, 4, 4, 4, 0, 0, 0, 0, 18, 18, 18, 0, 0, 6, 6, 0, 18, 12, 0, 4, 4, 4, 0, 0, 0, 18, 18, 6, 4, 4, 4, 6, 8, 6, 6, 0, 14, 2, 0, 8, 6, 6, 0, 4, 0, 0, 0, 0, 0, 4, 8, 8, 8, 4, 18, 0, 0, 4, 10, 18, 6, 0, 0, 18, 10, 10, 6, 2, 4, 4, 10, 10, 10, 2, 8, 0, 0, 0, 0, 10, 6, 6, 0, 4, 4, 0, 0, 0, 0, 8, 0, 0, 4, 4, 6, 6, 10, 6, 0, 0, 6, 4, 4, 8, 0, 12, 6, 2, 2, 8, 8, 4, 4, 4, 4, 6, 2, 2, 4, 0, 6, 0, 0, 0, 12, 18, 8, 0, 0, 4, 4, 2, 0, 0, 0, 0, 18, 
12, 6, 6, 4, 4, 12, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 6, 18, 0, 0, 18, 6, 4, 2, 2, 0, 0, 10, 0, 0, 0, 12, 4, 4, 4, 4, 4, 8, 18, 6, 18, 18, 12, 12, 12, 0, 0, 0, 0, 10, 12, 12, 12, 12, 12, 4, 4, 4, 6, 6, 6, 6, 12, 0, 6, 0, 0, 4, 4, 18, 18, 18, 0, 0, 4, 6, 6, 0, 0, 2, 0, 0, 0, 18, 12, 12, 0, 0, 0, 0, 0, 0, 18 [... truncated]

There are no missing values, the response is a 4 level factor and inputs are the following:

 Classes ‘tbl_df’, ‘tbl’ and 'data.frame':  1165 obs. of  14 variables:
 $ relatedness_cottle       : num  0 0 8 8 0 6 0 6 6 0 ...
 $ dominance_cottle         : int  4 6 0 6 6 6 6 4 4 4 ...
 $ time_spent               : num  26832 20822 18893 13107 25406 ...
 $ num_color_changes        : num  3.33 2.33 1.33 1 1 ...
 $ num_selects              : num  1 0.667 2 0.667 1.667 ...
 $ show_select_match        : num  1 0.667 0.333 1 1 ...
 $ default_size             : num  0.667 0 0.667 0 0 ...
 $ select_order             : Factor w/ 6 levels "future_past_present",..: 1 4 4 2 5 1 4 6 6 4 ...
 $ order_x                  : Factor w/ 6 levels "future_past_present",..: 4 4 4 4 4 3 4 4 4 4 ...
 $ color_past               : Factor w/ 8 levels "black","blue",..: 5 1 6 8 5 7 1 6 6 5 ...
 $ color_present            : Factor w/ 8 levels "black","blue",..: 1 4 4 4 6 8 4 4 1 4 ...
 $ color_future             : Factor w/ 8 levels "black","blue",..: 2 2 2 2 2 2 1 2 8 2 ...
 $ dominance_cottle_future  : int  0 4 0 4 2 0 4 2 2 0 ...
 $ relatedness_cottle_future: int  0 2 4 4 0 4 0 2 4 0 ...

But if I call gbm directly with the dataframe, it works:

summary(gbm(clusterAssignment[trainIndex] ~ ., data = my_data[trainIndex,vars_for_clust]))
Distribution not specified, assuming multinomial ...
                                                var   rel.inf
color_present                         color_present 33.533673
dominance_cottle                   dominance_cottle 33.170138
default_size                           default_size 25.321566
dominance_cottle_future     dominance_cottle_future  5.674563
color_future                           color_future  2.300060
relatedness_cottle               relatedness_cottle  0.000000
time_spent                               time_spent  0.000000
num_color_changes                 num_color_changes  0.000000
num_selects                             num_selects  0.000000
show_select_match                 show_select_match  0.000000
select_order                           select_order  0.000000
order_x                                     order_x  0.000000
color_past                               color_past  0.000000
relatedness_cottle_future relatedness_cottle_future  0.000000

Edit: to reproduce, run the script found here.

Answer 1

For now, casting a dataframe from plyr/dplyr to a normal dataframe with as.data.frame() fixes the problem.

train(as.data.frame(issueDataframe), issueResponse, method="gbm")

See this issue.

Answer 2

same problem with the glm method. Solved when I remove the VERBOSE option...