eiCompare (version 3.0.0)

md_bayes_gen: MD Bayes Generalize

Description

Tunes and estimates MD Bayes algorithm (ei.MD.bayes). This, combined with md_bayes_table() produces tables of results compatible with EI table of results.

Usage

md_bayes_gen(
  dat,
  form,
  total_yes = TRUE,
  total,
  ntunes = 10,
  totaldraws = 10000,
  seed = 12345,
  sample = 1000,
  thin = 100,
  burnin = 10000,
  ret_mcmc = TRUE,
  ci = c(0.025, 0.975),
  ci_true = TRUE,
  produce_draws = FALSE,
  ...
)

Arguments

dat

data.frame() object of just raw candidate vote and raw population counts. Put vote results in first set of columns, put population counts next

form

Formula object, e.g.: cbind(V1, V2, novote) ~ cbind(VtdAVap_cor, VtdBVap_cor, VtdHVap_cor, VtdOVap_cor)

total_yes

Logical, default=TRUE. Include total variable from data? Usually when data are stored in percents

total

character, total variable column name

ntunes

Numeric. How much to tune tuneMD. Default = 10

totaldraws

Numeric. Number of total draws from MD. Default = 10000

seed

Numeric. Default = 12345

sample

Numeric. Default = 10000

thin

Numeric. Default = 10

burnin

Numeric. Default = 10000

ret_mcmc

Logical. Default = TRUE

ci

numeric vector of credible interval (low/high), default is 95 percent= c(0.025, 0.975)

ci_true

Logical, default = TRUE. Include credible intervals in reported results.

produce_draws

Logical, default is FALSE. Produces two-item list of table and md.bayes() mcmc draws (for additional testing and analysis)

...

Additional arguments passed to tuneMD() and ei.MD.bayes()

Value

List object of length 1 (when produce_draws=FALSE). List object of length 2 (when produce_draws=TRUE). First item is list of race x candidate tabular results, with mean, SE, and credible intervals. Second item is mcmc draws.

References

eiPack, King et. al. (http://gking.harvard.edu/eiR)

Examples

# NOT RUN {
# }
# NOT RUN {
# TOY DATA EXAMPLE
canda <- c(10, 8, 10, 4, 8)
candb <- 20 - canda
white <- c(15, 12, 18, 6, 10)
black <- 20 - white
toy <- data.frame(canda, candb, white, black)

# Generate formula for passage to ei.reg.bayes() function #
form <- formula(cbind(canda, candb) ~ cbind(black, white))

# Then execute md_bayes_gen(); not run here due to time
md_bayes_gen(
  dat = toy,
  form = form,
  total_yes = FALSE,
  ntunes = 1,
  thin = 1,
  totaldraws = 100,
  sample = 10,
  burnin = 1
)

# Add in mcmc drawings
drawings <- md_bayes_gen(
  dat = toy,
  form = form,
  total_yes = FALSE,
  ntunes = 1,
  thin = 1,
  totaldraws = 100,
  sample = 10,
  burnin = 1,
  produce_draws = TRUE
)
head(drawings$draws)
# }