multinomial_em {imputeMulti}

EM algorithm for multinomial data

Description

Implement the EM algorithm for multivariate 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_em(
  x_y,
  z_Os_y,
  enum_comp,
  n_obs,
  conj_prior = c("none", "data.dep", "flat.prior", "non.informative"),
  alpha = NULL,
  tol = 5e-07,
  max_iter = 10000,
  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.

n_obs

An integer specifying the number of observations in the original data.

conj_prior

A string specifying the conjugate prior. One of c("none", "data.dep", "flat.prior", "non.informative").

alpha

The vector of counts \alpha for a Dir(\alpha) 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.

tol

A scalar specifying the convergence criteria. Defaults to 5e-7

max_iter

An integer specifying the maximum number of allowable iterations. Defaults to 10000.

verbose

Logical. If TRUE, provide verbose output on each iteration.

Value

An object of class mod_imputeMulti-class.

See Also

multinomial_data_aug, 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")

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

## End(Not run)