hb_estimation_parallel: Bayesian hierarchical model to estimate proportion of votes...

In cotecora-team-2/quickcountmx: Quick-count estimation package for the 2021 Mexican election

Bayesian hierarchical model to estimate proportion of votes allocated to each party

Description

Compute point estimate and credible intervals for each candidate, parallized over split_var.

Usage

hb_estimation_parallel(
  data_tbl,
  sampling_frame,
  split_var = NULL,
  nominal_list_var = NULL,
  num_cores = 5,
  inv_metric_list = NULL,
  nominal_max = 1000,
  sig_figs = 7,
  ...
)

Arguments

data_tbl	tibble of sample data.
sampling_frame	tibble of sampling frame with stratum variable (named exactly as in data_tbl) and covariates.
split_var	unquoted name of variable that splits sampling frame and data
nominal_list_var	Unquoted name of variable with nominal list of voters
num_cores	Number of cores to use for parallel computation
inv_metric_list	List of inv_metric diagonals guesses for each split
nominal_max	Maximum number of nominal count for stations. Used for stations without fixed nominal list.
sig_figs	Number of significant figures for Stan output
...	Other parameters passed to hb_estimation.

Details

Posterior simulations of parameters are computed using stan, and each party's votes are simulated for every polling station (logit model) or with a softmax link for the default (mlogit model). There is one independent model for each party, and proportions are calculated from posterior simulations of total votes. Splits are modelled independently

Value

A list with model fit (if return_fit=TRUE), a tibble estimates including point estimates for each party (median) and limits of credible intervals, and a vector inv_metric for the model

cotecora-team-2/quickcountmx documentation built on July 17, 2025, 5:14 a.m.