categorical | R Documentation |

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:

- cutpoints
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:

- strata
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.

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.

`missing_variable`

# 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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