#' Function to calculate partial dependencies from a random forest models using
#' a nested tibble.
#'
#' @param df_nest Nested tibble created by \code{\link{rmw_model_nested_sets}}.
#'
#' @param variables Vector of variables to calculate partial dependencies for.
#'
#' @param n_cores Number of CPU cores to use for the model calculations.
#'
#' @param training_only Should only the training set be used for prediction?
#'
#' @param rename Within the \code{partial_dependencies} nested tibble, should
#' the generic \code{"variable"} name be renamed to \code{"variable_model"}.
#' This is useful when \code{"variable"} has been used as a pollutant identifier.
#'
#' @param verbose Should the function give messages?
#'
#' @author Stuart K. Grange
#'
#' @seealso \code{\link{rmw_nest_for_modelling}},
#' \code{\link{rmw_model_nested_sets}}, \code{\link{rmw_partial_dependencies}}
#'
#' @return Nested tibble.
#'
#' @export
rmw_predict_nested_partial_dependencies <- function(df_nest,
variables = NA,
n_cores = NA,
training_only = TRUE,
rename = FALSE,
verbose = FALSE) {
# Check input
if (!all(c("observations", "model") %in% names(df_nest))) {
cli::cli_abort("Input requires `observations` and `model` variables.")
}
# Predict the partial dependencies
df_nest <- df_nest %>%
mutate(
partial_dependencies = list(
rmw_partial_dependencies(
model,
observations,
variable = variables,
training_only = training_only,
n_cores = n_cores,
verbose = verbose
)
)
)
# Rename the variable within partial dependencies unit
if (rename) {
df_nest <- df_nest %>%
mutate(
partial_dependencies = list(
rename(partial_dependencies, variable_model = variable)
)
)
}
return(df_nest)
}
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