Extrapolate

Extrapolate to dimensions beyond the range of those which exist in your data.table. Uses linear, rate of change, or uniform extrapolation.

extrapolate(
  dt,
  id_cols,
  extrapolate_col,
  value_col,
  extrapolate_vals,
  method,
  n_groups_fit,
  n_groups_bin = NULL
)

Arguments

dt	[data.table()] Data you would like to extrapolate.
id_cols	[characher()] Columns which uniquely identify rows of dt. Extrapolation will be done separately by group on id_cols excluding interpolate_col.
extrapolate_col	[characher(1)] Name of column of dt which is a numeric variable defining the groups you would like to extrapolate for.
value_col	[characher(1)] Name of a column of dt which is a numeric variable defining the values you would like to extrapolate.
extrapolate_vals	[numeric(1)] The values of extrapolate_col that you would like included in the outputs, including both input values and extrapolated values.
method	[characher(1)] The method for extrapolation. Must be either 'linear', 'rate_of_change', or 'uniform'.
n_groups_fit	[numeric(1)] The number of values of extrapolate_col to include in determining the extrapolation. For method 'linear' this is the groups used to fit a linear model. For method 'rate_of_change' this is the groups used to calculate a mean rate of change. For method 'uniform' this is the number of groups to combine into a uniform mean value which will be filled in. The groups will be selected as the first N groups or the last N groups in the data depending on the direction of extrapolation. Example: for extrapolation from years 1990:2000 up to year 2020 with n_groups_fit = 5, only years 1995:2000 will be used to fit the extrapolation model.
n_groups_bin	[numeric(1)] TODO: add option to bin groups before fitting linear model or calculating rate of change.

Value

[data.table()] dt with added rows for extrapolated values.

Details

For reference on rate of change, see page 12 of the Preston Demography book, or, https://www.un.org/esa/sustdev/natlinfo/indicators/methodology_sheets/demographics/population_growth_rate.pdf

Consider log transforming your data prior to extrapolation if appropriate.

Examples

dt <- data.table::data.table(
  group = c(rep("a", 5), rep("b", 5)),
  x = rep(c(1:5), 2),
  y = rep(seq(10, 50, 10), 2)
)
dt <- extrapolate(dt, id_cols = c("group", "x"), extrapolate_col = "x",
                  value_col = "y", extrapolate_vals = c(1:10),
                  method = "linear", n_groups_fit = 3)