continuous {mi}

Class "continuous"

Description

The continuous class inherits from the missing_variable-class and is the parent of the following classes: semi-continuous, censored-continuous, truncated-continuous, and bounded-continuous. The distinctions among these subclasses are given on their respective help pages. Aside from these facts, the rest of the documentation here is primarily directed toward developers.

Objects from the Classes

Objects can be created that are of class continuous via the missing_variable generic function by specifying type = "continuous"

Slots

The continuous class inherits from the missing_variable class and has the following additional slots:

transformation

Object of class "function" which is passed the raw_data slot and whose returned value is assigned to the data slot. By default, this function is the “standardize” transformation, using the mean and twice the standard deviation of the observed values

inverse_transformation

Object of class "function" which is the inverse of the function in the transformation slot.

transformed

Object of class "logical" of length one indicating whether the data slot is in the “transformed” state or the “untransformed” state

known_transformations

Object of class "character" indicating which transformations are possible for this variable

The fit_model method for a continuous variable is, by default, a wrapper for bayesglm and its family slot is, by default, gaussian

Author(s)

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.

See Also

missing_variable, semi-continuous-class, censored-continuous-class, truncated-continuous-class, bounded-continuous-class

Examples

# STEP 0: GET DATA
data(nlsyV, package = "mi")

# STEP 0.5 CREATE A missing_variable (you never need to actually do this)
income <- missing_variable(nlsyV$income, type = "continuous")
show(income)