ibind


This function combines two <code>mids</code> objects <code>x</code> and <code>y</code> into a
single <code>mids</code> object. The two <code>mids</code> objects should have the same
underlying multiple imputation model and should be fitted on exactly the same
dataset. If the number of imputations in <code>x</code> is <code>m(x)</code> and in
<code>y</code> is <code>m(y)</code> then the combination of both objects contains
<code>m(x)+m(y)</code> imputations.


manip

Multiple imputation using Fully Conditional Specification (FCS)
implemented by the MICE algorithm. Each variable has its own imputation
model. Built-in imputation models are provided for continuous data
(predictive mean matching, normal), binary data (logistic regression),
unordered categorical data (polytomous logistic regression) and ordered
categorical data (proportional odds). MICE can also impute continuous
two-level data (normal model, pan, second-level variables). Passive
imputation can be used to maintain consistency between variables. Various
diagnostic plots are available to inspect the quality of the imputations.

ibind: Combine imputations fitted to the same data

Description

Usage

Arguments

Value

See Also