Nothing

```
#' Functions to extract model statistics from a model calculated with
#' \code{rmw_calculate_model}.
#'
#' @param model A ranger model object from \code{rmw_calculate_model}.
#'
#' @param date_unix Should the \code{date_unix} variable be included in the
#' return?
#'
#' @return Tibble.
#'
#' @author Stuart K. Grange
#'
#' @examples
#'
#' # Extract statistics from the example random forest model
#' rmw_model_statistics(model_london)
#'
#' # Extract importances from a model object
#' rmw_model_importance(model_london)
#'
#' @export
rmw_model_statistics <- function(model) {
# Check class of object
stopifnot(inherits(model, "ranger"))
# Build tibble form some things in the model
tibble(
n_trees = model$num.trees,
mtry = model$mtry,
min_node_size = model$min.node.size,
importance_mode = model$importance.mode,
count_independent_variables = model$num.independent.variables,
independent_variables = stringr::str_c(
model$forest$independent.variable.names,
collapse = "; "
),
count_samples = model$num.samples,
prediction_error_mse = model$prediction.error,
r_squared = model$r.squared
)
}
#' @rdname rmw_model_statistics
#'
#' @details The variable importances are defined as "the permutation importance
#' differences of predictions errors". This measure is unit-less and the values
#' are not useful when comparing among data sets.
#'
#' @export
rmw_model_importance <- function(model, date_unix = TRUE) {
# Check class of object
stopifnot(inherits(model, "ranger"))
# Get vector and clean
vector_importance <- ranger::importance(model)
df <- data.frame(matrix(nrow = length(vector_importance), ncol = 2))
df <- setNames(df, c("variable", "importance"))
df[, 1] <- names(vector_importance)
df[, 2] <- vector_importance
df <- arrange(df, -importance)
# Drop date_unix before ranking
if (!date_unix) {
df <- filter(df, variable != "date_unix")
}
# Add ranking
df <- tibble::rowid_to_column(df, "rank")
# To tibble
df <- as_tibble(df)
return(df)
}
```

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