gen_agg_preds: Generate aggregate predictions

In ooelrich/oscbvar: Stuff for Bayesian vector autoregressions

Description

Super-function that generates aggregate predictions a la carte.

Usage

gen_agg_preds(
  atomic_df,
  start_agg,
  sotw,
  baseline = TRUE,
  caliper = TRUE,
  mahala = TRUE,
  cw = 5,
  mvc = 10,
  matching_vars = NULL
)

Arguments

atomic_df	Data frame containing the atomic predictions to base the aggregate predictions on.
start_agg	Which time point to generate the first aggregate prediction for.
sotw	Decision maker data set, observation one should correspond to start_t used to generate the atomic predictions.
baseline	Whether to generate the baseline aggregations (gewisano and equal weights). Defaults to TRUE.
caliper	Whether to generate RAP based on the caliper method. Defaults to TRUE.
mahala	Whether to generate RAP based on the mahalanobis method. Defaults to TRUE.
cw	Tolerance parameter for the caliper method. Defaults to 5. Caliper width.
mvc	Minimum viable cluster size, ie minimum amount of observations required within the cluster to not combine with the global mean.
matching_vars	Data frame with matching variables, ie pooling variables we want to fully match. First column should be t (and correspond in time to the other t columns).

ooelrich/oscbvar documentation built on Sept. 8, 2021, 3:31 p.m.