glance.mipo

Glance method to extract information from a <code>mipo</code> object

internal

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

Gerko Vink

Rianne Schouten

Alexander Robitzsch

Patrick Rockenschaub

Lisa Doove

Shahab Jolani

Margarita Moreno-Betancur

Ian White

Philipp Gaffert

Florian Meinfelder

Bernie Gray

Vincent Arel-Bundock

Mingyang Cai

Thom Volker

Edoardo Costantini

Caspar van Lissa

Hanne Oberman

Stephen Wade

Florian van Leeuwen

Frederik Fabricius-Bjerre

glance.mipo function

<dl><dt>x</dt>
<dd>An object with multiply-imputed models from <code>mice</code> (class: <code>mipo</code>)</dd>
<dt>...</dt>
<dd>extra arguments (not used)</dd></dl>

Arguments

Glance method to extract information from a mipo object — glance.mipo

<dl>

<dt>x</dt>
<dd>An object with multiply-imputed models from <code>mice</code> (class: <code>mipo</code>)</dd>


<dt>...</dt>
<dd>extra arguments (not used)</dd>

</dl>

Glance method to extract information from a <code>mipo</code> object

glance.mipo: Glance method to extract information from a `mipo` object

Description

Usage

Value

Arguments