h2o.aggregated_frame(model)

## Not run: 
library(h2o)
h2o.init()
df <- h2o.createFrame(rows = 100, 
                      cols = 5, 
                      categorical_fraction = 0.6, 
                      integer_fraction = 0,
                      binary_fraction = 0, 
                      real_range = 100, 
                      integer_range = 100, 
                      missing_fraction = 0)
target_num_exemplars = 1000
rel_tol_num_exemplars = 0.5
encoding = "Eigen"
agg <- h2o.aggregator(training_frame = df,
                     target_num_exemplars = target_num_exemplars,
                     rel_tol_num_exemplars = rel_tol_num_exemplars,
                     categorical_encoding = encoding)
# Use the aggregated frame to create a new dataframe
new_df <- h2o.aggregated_frame(agg)

## End(Not run)