make.blots

This helper function creates a valid <code>blots</code> object. The
<code>blots</code> object is an argument to the <code>mice</code> function.
The name <code>blots</code> is a contraction of blocks-dots.
Through <code>blots</code>, the user can specify any additional
arguments that are specifically passed down to the lowest level
imputation function.

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

make.blots function

<dl><dt>data</dt>
<dd>A <code>data.frame</code> with the source data</dd>
<dt>blocks</dt>
<dd>An optional specification for blocks of variables in
the rows. The default assigns each variable in its own block.</dd></dl>

Arguments

Creates a blots argument — make.blots

<dl>

<dt>data</dt>
<dd>A <code>data.frame</code> with the source data</dd>


<dt>blocks</dt>
<dd>An optional specification for blocks of variables in
the rows. The default assigns each variable in its own block.</dd>

</dl>

Creates a <code>blots</code> argument

make.blots: Creates a `blots` argument

Description

Usage

Value

Arguments

See Also

Examples