predict_average_fn: Impute data using simple averages

In gpw13/augury: Provides Streamlined Methods for Data Imputation and Forecasting for WHO DDI Statistics

Impute data using simple averages

Description

predict_average_fn() does simple imputation and flat extrapolation using averages grouped by average_cols.

Usage

predict_average_fn(
  df,
  col,
  average_cols = NULL,
  weight_col = NULL,
  flat_extrap = TRUE,
  test_col = NULL,
  group_col = NULL,
  obs_filter = NULL,
  pred_col = "pred",
  sort_col = NULL,
  sort_descending = FALSE,
  error_correct = FALSE,
  error_correct_cols = NULL,
  shift_trend = FALSE
)

Arguments

df	Data frame of model data.
col	Name of column to extrapolate/interpolate.
average_cols	Column name(s) of column(s) for use in grouping data for averaging, such as regions. If missing, uses global average of the data for infilling.
weight_col	Column name of column of weights to be used in averaging, such as country population.
flat_extrap	Logical value determining whether or not to flat extrapolate using the latest average for missing rows with no data available.
test_col	Name of logical column specifying which response values to remove for testing the model's predictive accuracy. If NULL, ignored. See model_error() for details on the methods and metrics returned.
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".
obs_filter	String value of the form "⁠logical operator⁠ integer" that specifies the number of observations required to fit the model and replace observations with predicted values. This is done in conjunction with group_col. So, if group_col = "iso3" and obs_filter = ">= 5", then for this model, predictions will only be used for iso3 vales that have 5 or more observations. Possible logical operators to use are >, >=, <, <=, ==, and !=. If `group_models = FALSE`, then `obs_filter` is only used to determine when predicted values replace observed values but **is not** used to restrict values from being used in model fitting. If `group_models = TRUE`, then a model is only fit for a group if they meet the `obs_filter` requirements. This provides speed benefits, particularly when running INLA time series using `predict_inla()`.
pred_col	Column name to store predicted value.
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".
sort_descending	Logical value on whether the sorted values from sort_col should be sorted in descending order. Defaults to FALSE.
error_correct	Logical value indicating whether or not whether mean error should be used to adjust predicted values. If TRUE, the mean error between observed and predicted data points will be used to adjust predictions. If error_correct_cols is not NULL, mean error will be used within those groups instead of overall mean error.
error_correct_cols	Column names of data frame to group by when applying error correction to the predicted values.
shift_trend	Logical value specifying whether or not to shift predictions so that the trend matches up to the last observation. If error_correct and shift_trend are both TRUE, shift_trend takes precedence.

Value

A data frame.

gpw13/augury documentation built on Oct. 15, 2024, 8:03 a.m.