The irrelevant class inherits from the
missing_variable-class and is used to
designate variables that are excluded from the models used to impute the missing values of
“relevant” variables. For example, if a survey has an “id” variable that
simply distinguishes observations, the user should designate it as irrelevant, although it
will automatically be classified so if its name is either “id” or starts with punctuation
(including underscores). The fixed class inherits from the irrelevant class and is used
for variables that are constant (within a sample). A variable that is instantiated from the
fixed class cannot have any missing values. The group class inherits from the fixed
class and is used like a
factor to spit samples in multilevel modeling; see
multilevel_missing_data.frame-class. None of these classes have an additional
slots. Aside from these facts, the rest of the documentation here is primarily directed toward developeRs.
missing_variable generic function can be used to
instantiate an object that inherits from the irrelevant class by specifying
type = "irrelevant",
type = "fixed", or
type = "group".
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) first <- missing_variable(as.factor(nlsyV$first), type = "group") show(first)
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