Run (tidy) Ecological Inference Estimation and Simulation

Usage

ei_(
  data,
  x,
  t,
  n,
  Zb = NULL,
  Zw = NULL,
  id = NA,
  erho = c(0.5, 3, 5, 0.1, 10),
  esigma = 0.5,
  ebeta = 0.5,
  ealphab = NA,
  ealphaw = NA,
  truth = NA,
  simulate = TRUE,
  ndraws = 99,
  nsims = 100,
  covariate = NULL,
  lambda1 = 4,
  lambda2 = 2,
  covariate.prior.list = NULL,
  tune.list = NULL,
  start.list = NULL,
  sample = 1000,
  thin = 1,
  burnin = 1000,
  verbose = 0,
  ret.beta = "r",
  ret.mcmc = TRUE,
  usrfun = NULL
)

Arguments

data: data where `x`, `t`, `total`, `Zb`, `Zw` are found
x: <[`data-masking`][dplyr_data_masking]> column of subgroup proportions in data
t: <[`data-masking`][dplyr_data_masking]> column of turnout in data
n: <[`data-masking`][dplyr_data_masking]> column of total in data
Zb: <[`data-masking`][dplyr_tidy_select]> columns of covariates in data
Zw: <[`data-masking`][dplyr_tidy_select]> columns of covariates in data
id: <[`data-masking`][dplyr_data_masking]> column of unique ids in data
erho: The standard deviation of the normal prior on \(\phi_5\) for the correlation. Numeric vector, used one at a time, in order. Default `c(.5, 3, 5, .1, 10)`.
esigma: The standard deviation of an underlying normal distribution, from which a half normal is constructed as a prior for both \(\breve{\sigma}_b\) and \(\breve{\sigma}_w\). Default \(= 0.5\)
ebeta: Standard deviation of the "flat normal" prior on \(\breve{B}^b\) and \(\breve{B}^w\). The flat normal prior is uniform within the unit square and dropping outside the square according to the normal distribution. Set to zero for no prior. Setting to positive values probabilistically keeps the estimated mode within the unit square. Default\(=0.5\)
ealphab: cols(Zb) x 2 matrix of means (in the first column) and standard deviations (in the second) of an independent normal prior distribution on elements of \(\alpha^b\). If you specify Zb, you should probably specify a prior, at least with mean zero and some variance (default is no prior). (See Equation 9.2, page 170, to interpret \(\alpha^b\)).
ealphaw: cols(Zw) x 2 matrix of means (in the first column) and standard deviations (in the second) of an independent normal prior distribution on elements of \(\alpha^w\). If you specify Zw, you should probably specify a prior, at least with mean zero and some variance (default is no prior). (See Equation 9.2, page 170, to interpret \(\alpha^w\)).
truth: A length(t) x 2 matrix of the true values of the quantities of interest.
simulate: default = TRUE:see documentation in eiPack for options for RxC ei.
ndraws: integer. The number of draws. Default is 99.
nsims: integer. The number of simulations within each draw. Default is 100.
covariate: see documentation in eiPack for options for RxC ei.
lambda1: default = 4:see documentation in eiPack for options for RxC ei.
lambda2: default = 2:see documentation in eiPack for options for RxC ei.
covariate.prior.list: see documentation in eiPack for options for RxC ei.
tune.list: see documentation in eiPack for options for RxC ei.
start.list: see documentation in eiPack for options for RxC ei.
sample: default = 1000
thin: default = 1
burnin: default = 1000
verbose: default = 0:see documentation in eiPack for options for RxC ei.
ret.beta: default = "r": see documentation in eiPack for options for RxC ei.
ret.mcmc: default = TRUE: see documentation in eiPack for options for RxC ei.
usrfun: see documentation in eiPack for options for RxC ei.

Value

ei_tbl

Examples

data(sample_ei)
dbuf <- ei_(sample_ei, x, t, n)
#> ℹ Running 2x2 ei
#> ℹ Maximizing likelihood for `erho` = 0.5.
#> ℹ Running 2x2 ei

#> ℹ Maximizing likelihood for `erho` = 3.
#> ℹ Running 2x2 ei

#> ℹ Maximizing likelihood for `erho` = 5.
#> ℹ Running 2x2 ei

#> ℹ Maximizing likelihood for `erho` = 0.1.
#> ℹ Running 2x2 ei

#> ✔ Running 2x2 ei [2ms]
#> 
#> ⠙ Beginning importance sampling.
#> ✔ Beginning importance sampling. [1.3s]
#>