Class "allcategorical_missing_data.frame"
This class inherits from the missing_data.frame-class but is customized for the situation where all the variables are categorical.
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 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.
The allcategorical_missing_data.frame class inherits from the missing_data.frame-class and
has three additional slots
Positive integer indicating the maximum number of latent classes
A list that holds the current realization of the unknown parameters
An object of unordered-categorical-class that contains
the current realization of the latent classes
Sophie Si for the algorithm and Ben Goodrich for the R implementation
rdf <- rdata.frame(n_full = 2, n_partial = 2,
restrictions = "stratified", types = "ord")
mdf <- missing_data.frame(rdf$obs, subclass = "allcategorical")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.