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Use averages to impute and forecast data

Source: R/predict_average.R
predict_average.Rd

predict_average() does simple infilling and prediction using averages. Similar to other predict functions, it also allows filling in of type and source if necessary. However, it does not provide confidence bounds on the estimates, like other predict_... model-based functions provide.

Usage

predict_average(
  df,
  col = "value",
  average_cols = NULL,
  weight_col = NULL,
  flat_extrap = TRUE,
  ret = c("df", "all", "error"),
  test_col = NULL,
  test_period = NULL,
  test_period_flex = NULL,
  group_col = "iso3",
  obs_filter = NULL,
  sort_col = "year",
  sort_descending = FALSE,
  pred_col = "pred",
  type_col = NULL,
  types = c("imputed", "imputed", "projected"),
  source_col = NULL,
  source = NULL,
  scenario_detail_col = NULL,
  scenario_detail = NULL,
  replace_obs = c("missing", "all", "none"),
  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.

ret

Character vector specifying what values the function returns. Defaults to returning a data frame, but can return a vector of model error, the model itself or a list with all 3 as components.

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.

test_period

Length of period to test for RMChE. If NULL, beginning and end points of each group in group_col are compared. Otherwise, test_period must be set to an integer n and for each group, comparisons are made between the end point and n periods prior.

test_period_flex

Logical value indicating if test_period is less than the full length of the series, should change error still be calculated for that point. Defaults to FALSE.

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()`.
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.

pred_col

Column name to store predicted value.

type_col

Column name specifying data type.

types

Vector of length 3 that provides the type to provide to data produced in the model. These values are only used to fill in type values where the dependent variable is missing. The first value is given to missing observations that precede the first observation, the second to those after the last observation, and the third for those following the final observation.

source_col

Column name containing source information for the data frame. If provided, the argument in source is used to fill in where predictions have filled in missing data.

source

Source to add to missing values.

scenario_detail_col

Column name containing scenario_detail information for the data frame. If provided, the argument in scenario_detail is used to fill in where prediction shave filled in missing data.

scenario_detail

Scenario details to add to missing values (usually the name of the model being used to generate the projection, optionally with relevant parameters).

replace_obs

Character value specifying how, if at all, observations should be replaced by infilled values. By default, replaces missing values in col but if set to "none" then col is not changed.

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

Depending on the value passed to ret, either a data frame with predicted data, a vector of errors from model_error(), a fitted model, or a list with all 3.

Details

For each year where at least 1 data point is available, the average is calculated as the prediction. If flat_extrap, then the latest average is flat extrapolated to the end of the data. When using test_col, the average may not be available for certain groups, so flat extrapolation will be relied on, meaning that the COR metric output by errors is difficult to interpret or use properly.