<p>An <code>R</code> object. Currently supported are methods for the
following classes: <code>mids</code>, <code>data.frame</code> and <code>matrix</code>. Also, 
<code>x</code> can be a vector.</p>

<p>Calculates the number of complete cases.</p>

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

Bernie Gray

ncc function

Number of complete cases — ncc

Number of complete cases

ncc: Number of complete cases

Description

Usage

Arguments

Value

See Also

Examples