biqq_es_k: BIQQ Model for k Clues for Origins of Electoral Systems...

In macartan/biqq: Package for BIQQ analysis as described in Jacobs, Humphreys (2016)

Description

Fit BIQQ Model for Origins of Electoral Systems example assuming that only k clues are sought

Usage

biqq_es_k(k, clue_data, fit = NULL, alpha_prior = rep(1, times = 4),
  pi_alpha = rep(1, times = 4), pi_beta = rep(1, times = 4),
  q0_alpha = rep(1, times = 4), q0_beta = rep(1, times = 4),
  q1_alpha = rep(1, times = 4), q1_beta = rep(1, times = 4),
  extract_pars = "abcd", out_fun = function(x) {     mean(x$abcd[, 2] -
  x$abcd[, 1]) }, iter = 8000, chains = 2, warmup = 1000,
  cores = 1)

Arguments

k	Integer. Number of clues sought
clue_data	Data frame. All known XYK cases from which only k cases are randomly drawn for BIQQ model
fit	Stan model fit object
alpha_prior	Numeric vector of length 4. Dirichlet distribution parameters for proportions of 4 possible types in the population. Defaults to c(1,1,1,1)
pi_alpha	Numeric vector of length 4. Alpha shape parameters for Beta distribution of probabilities of assignment for 4 possible types in the population. Defaults to c(1,1,1,1)
pi_beta	Numeric vector of length 4. Beta shape parameters for Beta distribution of probabilities of assignment for 4 possible types in the population. Defaults to c(1,1,1,1)
q0_alpha	Numeric vector of length 4. Alpha shape parameters for Beta distribution of probabilities of not observing clue given that it was sought and any of four possible types. Defaults to c(1,1,1,1)
q0_beta	Numeric vector of length 4. Beta shape parameters for Beta distribution of probabilities of not observing clue given that it was sought and any of four possible types. Defaults to c(1,1,1,1)
q1_alpha	Numeric vector of length 4. Alpha shape parameters for Beta distribution of probabilities of observing clue given that it was sought and any of four possible types. Defaults to c(1,1,1,1)
q1_beta	Numeric vector of length 4. Beta shape parameters for Beta distribution of probabilities of observing clue given that it was sought and any of four possible types. Defaults to c(1,1,1,1)
extract_pars	Character vector. Names of posterior parameters to extract as taken by extract function. These should be sufficient to compute output of interest for BIQQ program using out_fun. Defaults to extract_pars = "abcd", which implies a call rstan::extract(posterior, pars = "abcd")
out_fun	Function. The function, which takes posterior samples extracted using extract_pars as an argument and returns quantity/ies of interest. Defaults to out_fun = function(x) { mean(x$abcd[,2] - x$abcd[,1]) } , which returns mean of posterior difference between proportions of beneficial and adverse types in the population. This function is used in the replication of Kreuzer study presented in Humphreys, Jacobs (2016)
iter	Integer. Total number of iterations in each chain. Defaults to 20000,
chains	Integer. Number of MC chains. Defaults to 2
warmup	Integer. Number of warm-up iterations in each chain. Defaults to 5000
cores	Integer. Number of cores to use for parallel computation. Defaults to 1

Examples

## Not run: 

library(biqq)
es_fit <-
  init_biqq(model_code = stan_es,
            data = data_es_init)

sims <- 10 # Number of simulations
N <- nrow(data_es) # Number of cases

# Generate hyperparameters for Stan
q0_alpha_beta <- mapply(m = c(q.a0 = 0.1, # Assumptions on mean of q0's
                              q.b0 = 0.1,
                              q.c0 = 0.05,
                              q.d0 = 0.3),
                        sd = rep(.01, times = 4),
                        FUN = beta_prior)

q1_alpha_beta <- mapply(m = c(q.a1 = 0.95, # Assumptions on mean of q1's
                              q.b1 = 0.9,
                              q.c1 = 0.475,
                              q.d1 = 0.5),
                        sd = rep(.01, times = 4),
                        FUN = beta_prior)
# Rstan setup options
rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores())

# Run the analysis
betas <-
  parallel::mclapply(X          = rep(0:N, each = sims),
                     FUN        = biqq::biqq_es_k,
                     fit        = es_fit,
                     clue_data  = data_es,
                     q0_alpha   = q0_alpha_beta["alpha",],
                     q0_beta    = q0_alpha_beta["beta",],
                     q1_alpha   = q1_alpha_beta["alpha",],
                     q1_beta    = q1_alpha_beta["beta",],
                     chains     = 2,
                     cores      = 1,
                     extract_pars = "abcd",
                     out_fun = function(x) { mean(x$abcd[,2] - x$abcd[,1]) })

betas <- matrix(unlist(betas), ncol = sims, byrow = TRUE)

## End(Not run)

macartan/biqq documentation built on May 6, 2019, 6:03 p.m.