fit glmertree() to a cross-validated data set

fit glmertree() to a cross-validated data set

cross_validate_it(cv_obj, seed = 713, mod_formula, tuning_grid = NULL, ...)

Arguments

cv_obj

vfold_cv—a v-fold cross-validated dataset from rsample::vfold_cv()

seed

integer—starting seed

mod_formula

Formula—made from as.Formula(). uppercase required :)

tuning_grid

— either tuning grid e.g., from dials::grid_max_entropy() or default grid (when tuning_grid = NULL).

...

additional arguments to be passed to glmertree()

Value

tibble—fit statistics (rmse, mae) for object

Examples

dat <- sim_multilevel()
example_split <- rsample::initial_split(dat)
example_train <- rsample::training(example_split)
example_test  <-  rsample::testing(example_split)
cv <- rsample::vfold_cv(data = example_train, v = 10)

ex_formula <-
   Formula::as.Formula(
      'outcome ~ small_1 |
      (1 | id_vector) |
      small_c_1 + small_c_2 + nuisance_1a + nuisance_c_1a'
      )

tuning_grid <-
  dials::grid_max_entropy(
    maxdepth_par(maxdepth_min = 0L, maxdepth_max = 20L),
    alpha_par(alpha_min = 0.10, alpha_max = 0.001),
    trim_par(trim_min = 0.01, trim_max = 0.5),
    size = 10
  )

fitted <-
   cross_validate_it(
      cv_obj = cv,
      seed = 713,
      tuning_grid = tuning_grid,
      mod_formula = ex_formula
      )
#> cv index 1 complete
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