multinomial_data_aug: Data Augmentation algorithm for multinomial data

In alexWhitworth/imputeMulti: Imputation Methods for Multivariate Multinomial Data

Data Augmentation algorithm for multinomial data

Description

Implement the Data Augmentation algorithm for multvariate multinomial data given observed counts of complete and missing data (Y_obs and Y_mis). Allows for specification of a Dirichlet conjugate prior.

Usage

multinomial_data_aug(
  x_y,
  z_Os_y,
  enum_comp,
  conj_prior = c("none", "data.dep", "flat.prior", "non.informative"),
  alpha = NULL,
  burnin = 100,
  post_draws = 1000,
  verbose = FALSE
)

Arguments

x_y	A data.frame of observed counts for complete observations.
z_Os_y	A data.frame of observed marginal-counts for incomplete observations.
enum_comp	A data.frame specifying a vector of all possible observed patterns.
conj_prior	A string specifying the conjugate prior. One of c("none", "data.dep", "flat.prior", "non.informative").
alpha	The vector of counts α for a Dir(α) prior. Must be specified if conj_prior is either c("data.dep", "flat.prior"). If flat.prior, specify as a scalar. If data.dep, specify as a vector with key matching enum_comp.
burnin	A scalar specifying the number of iterations to use as a burnin. Defaults to 100.
post_draws	An integer specifying the number of draws from the posterior distribution. Defaults to 1000.
verbose	Logical. If TRUE, provide verbose output on each iteration.

Value

An object of class mod_imputeMulti-class.

See Also

multinomial_em, multinomial_impute

Examples

## Not run: 
 data(tract2221)
 x_y <- multinomial_stats(tract2221[,1:4], output= "x_y")
 z_Os_y <- multinomial_stats(tract2221[,1:4], output= "z_Os_y")
 x_possible <- multinomial_stats(tract2221[,1:4], output= "possible.obs")

 imputeDA_mle <- multinomial_data_aug(x_y, z_Os_y, x_possible, n_obs= nrow(tract2221),
                     conj_prior= "none", verbose= TRUE)

## End(Not run)

alexWhitworth/imputeMulti documentation built on July 15, 2022, 7:02 p.m.