Use mean error to correct predictions

Usage

error_correct_fn(
  df,
  response,
  group_col,
  sort_col,
  sort_descending,
  pred_col,
  pred_upper_col,
  pred_lower_col,
  test_col,
  error_correct,
  error_correct_cols,
  shift_trend
)

Arguments

df: Data frame of model data.
response: Column name of response variable.
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".
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.
pred_upper_col: Column name to store upper bound of confidence interval generated by the predict_... function. This stores the full set of generated values for the upper bound.
pred_lower_col: Column name to store lower bound of confidence interval generated by the predict_... function. This stores the full set of generated values for the lower bound.
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.
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.