rmw_predict_nested_partial_dependencies: Function to calculate partial dependencies from a random...

In rmweather: Tools to Conduct Meteorological Normalisation and Counterfactual Modelling for Air Quality Data

Function to calculate partial dependencies from a random forest models using a nested tibble.

Description

Function to calculate partial dependencies from a random forest models using a nested tibble.

Usage

rmw_predict_nested_partial_dependencies(
  df_nest,
  variables = NA,
  n_cores = NA,
  training_only = TRUE,
  rename = FALSE,
  verbose = FALSE,
  progress = FALSE
)

Arguments

df_nest	Nested tibble created by rmw_model_nested_sets.
variables	Vector of variables to calculate partial dependencies for.
n_cores	Number of CPU cores to use for the model calculations.
training_only	Should only the training set be used for prediction?
rename	Within the partial_dependencies nested tibble, should the generic "variable" name be renamed to "variable_model". This is useful when "variable" has been used as a pollutant identifier.
verbose	Should the function give messages?
progress	Should a progress bar be displayed?

Value

Nested tibble.

Author(s)

Stuart K. Grange

See Also

rmw_nest_for_modelling, rmw_model_nested_sets, rmw_partial_dependencies

rmweather documentation built on June 22, 2024, 9:33 a.m.