flat_extrapolation: Flat extrapolation
In gpw13/billionaiRe: Calculate the WHO Triple Billions

flat_extrapolation

R Documentation

Flat extrapolation

Description

Flat extrapolation

Usage

flat_extrapolation(
  df,
  col,
  group_col = NULL,
  sort_col = "year",
  pred_col = "pred",
  sort_descending = FALSE,
  replace_obs = c("missing", "none")
)

Arguments

`df`	Data frame of model data
`col`	Name of column to extrapolate/interpolate.
`group_col`	Column name(s) of group(s) to use in `dplyr::group_by()` when supplying type, calculating mean absolute scaled error on data involving time series, and if `group_models`, then fitting and predicting models too. If `NULL`, not used. Defaults to `"iso3"`.
`sort_col`	Column name(s) to use to `dplyr::arrange()` the data prior to supplying type and calculating mean absolute scaled error on data involving time series. If `NULL`, not used. Defaults to `"year"`.
`pred_col`	Column name to store predicted value.
`sort_descending`	Logical value on whether the sorted values from `sort_col` should be sorted in descending order. Defaults to `FALSE`.
`replace_obs`	Character value specifying how, if at all, observations should be replaced by fitted values. Defaults to replacing only missing values, but can be used to replace all values or none.

Value

a data frame with predicted data,

General scenario functions add_scenario(), calculate_aarc(), calculate_aroc(), exec_scenario(), fill_cols_scenario(), get_aarr(), get_baseline_value(), get_baseline_year(), get_last_value(), get_last_year_scenario(), get_latest_aarc(), get_percent_change_aarc(), get_target_aarc(), remove_unwanted_scenarios(), trim_values(), trim_years()