The categorical class is a virtual class that inherits from the missing_variable-class
and is the parent of the unordered-categorical and ordered-categorical classes. The ordered-categorical
class is the parent of both the binary and interval classes. Aside from these facts, the rest of the
documentation here is primarily directed toward developers.
The categorical class is virtual, so no objects
may be created from it. However, the missing_variable generic function can be used to
instantiate an object that inherits from the categorical class by specifying
type = "unordered-categorical", type = "ordered-categorical",
type = "binary", type = "grouped-binary", or type = "interval".
The unordered-categorical class inherits from the categorical class and has no additional slots
but must have more than two uniquely observed values in its raw_data slot. The default fit_model
method is a wrapper for the multinom function in the nnet package. The ordered-categorical
class inherits from the categorical class and has one additional slot:
Object of class "numeric" which is a vector of thresholds (sometimes estimated) that
govern how an assumed latent variable is divided into observed ordered categories
The fit_model method for an ordered-categorical variable is, by default, a wrapper for
bayespolr. The binary class inherits from the ordered-categorical class and has no additional slots.
It must have exactly two uniquely observed values in its raw_data slot and its fit_model method is,
by default, a wrapper for bayespolr. The grouped-binary class inherits from the binary class and
has one additional slot:
Object of class "character" which is a vector (possibly of length one) of variable names that
group the observations into strata. The named external variables should also be categorical.
The default fit_model method for a grouped-binary variable is a wrapper for the clogit
function in the survival package and the variables named in the strata slot are passed to the
strata function.
The interval class inherits from the ordered-categorical class, has no additional slots, and is intended for variables
whose observed values are only known up to orderable intervals. Its fit_model method is, by default, a
wrapper for survreg even though it may or may not be a “survival” model in any meaningful sense.
# NOT RUN {
# STEP 0: GET DATA
data(nlsyV, package = "mi")
# STEP 0.5 CREATE A missing_variable (you never need to actually do this)
momrace <- missing_variable(as.factor(nlsyV$momrace), type = "unordered-categorical")
show(momrace)
# }
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