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Resolve bootstrap method including use of the boot package

Source: R/resolve_bootstrap_method.R
resolve_bootstrap_method.Rd

Resolves the effective bootstrap method to be used by bootstrap_cube(), combining:

Usage

resolve_bootstrap_method(
  df,
  fun,
  ...,
  cat_var,
  ref_group = NA,
  method = "smart"
)

Arguments

df

A dataframe.

fun

A function which, when applied to df, returns the statistic(s) of interest. This function must return a dataframe with a column diversity_val.

...

Additional arguments passed to fun.

cat_var

A character vector specifying the grouping variable(s) used by fun.

ref_group

A value indicating the reference group. If NA (default), bootstrapping may be delegated to the boot package.

method

Character string specifying the bootstrap method. One of "whole_cube", "group_specific", "boot_whole_cube", "boot_group_specific", or "smart" (default).

Value

A single character string giving the resolved bootstrap method:

  • "whole_cube"

  • "group_specific"

  • "boot_whole_cube"

  • "boot_group_specific"

Details

  • the scope of the indicator (group-specific vs whole-cube), and

  • whether a reference group is used.

When method = "smart", the scope of the indicator is inferred using derive_bootstrap_method(). If no reference group is specified (ref_group = NA) and exactly one grouping variable is used (length(cat_var) == 1), the corresponding boot_* method is selected.

The resolution follows these rules:

  1. If method is not "smart", it is returned unchanged.

  2. If method = "smart", the indicator scope is inferred using derive_bootstrap_method().

  3. If more than one grouping variable is specified (length(cat_var) > 1), bootstrapping via the boot package is disabled and the inferred non-boot method is returned.

  4. If exactly one grouping variable is used and ref_group = NA, the resolved method is prefixed with "boot_", resulting in "boot_group_specific" or "boot_whole_cube".

  5. If a reference group is specified, the non-boot variants "group_specific" or "whole_cube" are returned.

See also

Other indicator_uncertainty_helper: boot_list_to_dataframe(), bootstrap_cube_raw(), calculate_boot_ci_from_boot(), derive_bootstrap_method()

Examples

# Example 1: Group-specific indicator without a reference group
# Mean sepal length per species (calculated independently per group)
mean_sepal_length <- function(x) {
  out_df <- aggregate(Sepal.Length ~ Species, x, mean)
  names(out_df) <- c("Species", "diversity_val")
  out_df
}

resolve_bootstrap_method(
  df = iris,
  fun = mean_sepal_length,
  cat_var = "Species",
  ref_group = NA,
  method = "smart"
)
#> [1] "boot_group_specific"

# Example 2: Group-specific indicator with a reference group
resolve_bootstrap_method(
  df = iris,
  fun = mean_sepal_length,
  cat_var = "Species",
  ref_group = "setosa",
  method = "smart"
)
#> [1] "group_specific"

# Example 3: Indicator that depends on the whole cube
# The statistic per species depends on all species together
scaled_sepal_length <- function(x) {
  out_df <- aggregate(Sepal.Length ~ Species, x, mean)
  out_df$Sepal.Length <- out_df$Sepal.Length / nrow(out_df)
  names(out_df) <- c("Species", "diversity_val")
  out_df
}

resolve_bootstrap_method(
  df = iris,
  fun = scaled_sepal_length,
  cat_var = "Species",
  ref_group = NA,
  method = "smart"
)
#> [1] "boot_whole_cube"