eiCompare (version 3.0.4)

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,
  ...
)

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.

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()

Author

Loren Collingwood <[e-mail]>

References

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

Examples

# \donttest{
# 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)
# }