ibind

<p>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.</p>

manip

Multiple imputation using Fully Conditional Specification (FCS)
implemented by the MICE algorithm as described in Van Buuren and
Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>. 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.

Stef van Buuren

mice

Multivariate Imputation by Chained Equations

Karin Groothuis-Oudshoorn

Alexander Robitzsch

Gerko Vink

Lisa Doove

Shahab Jolani

Rianne Schouten

Philipp Gaffert

Florian Meinfelder

ibind function

Combine imputations fitted to the same data — ibind

Combine imputations fitted to the same data

ibind: Combine imputations fitted to the same data

Description

Usage

Arguments

Value

See Also