imputeMulti (version 0.4.5)

multinomial_data_aug: 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 = 500,
  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 $\alpha$ 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 wi

burnin

A scalar specifying the number of iterations to use as a burnin. Defaults to 500.

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

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(tract_2221),
                     conj_prior= "none", verbose= TRUE)