ampute.mcar

This function creates a missing data indicator for each pattern, based on a MCAR
missingness mechanism. The function is used in the multivariate amputation function
<code>ampute</code>.

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

ampute.mcar function

<dl><dt>P</dt>
<dd>A vector containing the pattern numbers of the cases' candidates.
For each case, a value between 1 and #patterns is given. For example, a
case with value 2 is candidate for missing data pattern 2.</dd>
<dt>patterns</dt>
<dd>A matrix of size #patterns by #variables where <code>0</code> indicates
a variable should have missing values and <code>1</code> indicates a variable should
remain complete. The user may specify as many patterns as desired. One pattern
(a vector) is also possible. Could be the result of <code>ampute.default.patterns</code>,
default will be a square matrix of size #variables where each pattern has missingness
on one variable only.</dd>
<dt>prop</dt>
<dd>A scalar specifying the proportion of missingness. Should be a value
between 0 and 1. Default is a missingness proportion of 0.5.</dd></dl>

Arguments

Author

Multivariate amputation under a MCAR mechanism — ampute.mcar

<dl>

<dt>P</dt>
<dd>A vector containing the pattern numbers of the cases' candidates.
For each case, a value between 1 and #patterns is given. For example, a
case with value 2 is candidate for missing data pattern 2.</dd>


<dt>patterns</dt>
<dd>A matrix of size #patterns by #variables where <code>0</code> indicates
a variable should have missing values and <code>1</code> indicates a variable should
remain complete. The user may specify as many patterns as desired. One pattern
(a vector) is also possible. Could be the result of <code>ampute.default.patterns</code>,
default will be a square matrix of size #variables where each pattern has missingness
on one variable only.</dd>


<dt>prop</dt>
<dd>A scalar specifying the proportion of missingness. Should be a value
between 0 and 1. Default is a missingness proportion of 0.5.</dd>

</dl>

Multivariate amputation under a MCAR mechanism

ampute.mcar: Multivariate amputation under a MCAR mechanism

Description

Usage

Value

Arguments

Author

See Also