R: Class "allcategorical_missing_data.frame"

allcategorical_missing_data.frame {mi} | R Documentation

## Class "allcategorical_missing_data.frame"

### Description

This class inherits from the `missing_data.frame-class` but is customized for the situation where all the variables are categorical.

### Details

The `fit_model-methods` for the allcategorical_missing_data.frame class implement a Gibbs sampler. However, it does not utilize any ordinal information that may be available. Continuous variables should be made into factors using the `cut` command before calling `missing_data.frame`.

### Objects from the Class

Objects can be created by calls of the form `new("allcategorical_missing_data.frame", ...)`. However, its users almost always will pass a `data.frame` to the `missing_data.frame` function and specify the `subclass` argument.

### Slots

The allcategorical_missing_data.frame class inherits from the `missing_data.frame-class` and has three additional slots

Hstar

  Positive integer indicating the maximum number of latent classes

parameters

  A list that holds the current realization of the unknown parameters

latents

  An object of `unordered-categorical-class` that contains the current realization of the latent classes

### Author(s)

Sophie Si for the algorithm and Ben Goodrich for the R implementation

### See Also

`missing_data.frame`

### Examples

    rdf <- rdata.frame(n_full = 2, n_partial = 2,
                       restrictions = "stratified", types = "ord")
    mdf <- missing_data.frame(rdf$obs, subclass = "allcategorical")

[Package mi version 1.0 Index]