smoketest_floor_elpd_invariance: Smoke Test for FLOOR ELPD Invariance

In bivarhr: Bivariate Hurdle Regression with Bayesian Model Averaging

Smoke Test for FLOOR ELPD Invariance

Description

Tests that the ELPD ranking is invariant to different FLOOR penalty values in the Stan model.

Usage

smoketest_floor_elpd_invariance(
  DT,
  stan_code,
  floors = c(-1e+06, -1e+08, -10000),
  spec = "C",
  controls = character(0),
  k_grid = 0:1,
  hs_grid = data.frame(hs_tau0 = c(0.1, 0.5), hs_slab_scale = c(1, 5), hs_slab_df = 4),
  hs_rows = 1:2,
  iter_warmup = 200,
  iter_sampling = 200,
  chains = 2,
  seed = 123,
  verbose = TRUE
)

Arguments

DT	Data.table with the data.
stan_code	Character; Stan model code.
floors	Numeric vector of FLOOR values to test.
spec	Character; model specification.
controls	Character vector of control variables.
k_grid	Integer vector of lag values to test.
hs_grid	Data.frame with horseshoe hyperparameter grid.
hs_rows	Integer vector; which rows of hs_grid to use.
iter_warmup	Integer; warmup iterations.
iter_sampling	Integer; sampling iterations.
chains	Integer; number of chains.
seed	Integer; random seed.
verbose	Logical; print progress messages.

Value

A list with components:

same_order	Logical; TRUE if ranking is identical across all FLOOR values.
floors	The tested FLOOR values.
tables	List of result tables for each FLOOR.
combined	Combined data.frame of all results.
rank_signatures	Character vector of ranking signatures.

bivarhr documentation built on July 7, 2026, 1:06 a.m.