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.
# 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)