The categorical class is a virtual class that inherits from the
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
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
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.
fit_model method for a grouped-binary variable is a wrapper for the
function in the survival package and the variables named in the strata slot are passed to the
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
survreg even though it may or may not be a “survival” model in any meaningful sense.
Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman.
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