norm.draw

.norm.draw

This function draws random values of beta and sigma under the Bayesian
linear regression model as described in Rubin (1987, p. 167). This function
can be called by user-specified imputation functions.

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

norm.draw function

<dl><dt>y</dt>
<dd>Incomplete data vector of length <code>n</code></dd>
<dt>ry</dt>
<dd>Vector of missing data pattern (<code>FALSE</code>=missing,
<code>TRUE</code>=observed)</dd>
<dt>x</dt>
<dd>Matrix (<code>n</code> x <code>p</code>) of complete covariates.</dd>
<dt>rank.adjust</dt>
<dd>Argument that specifies whether <code>NA</code>'s in the
coefficients need to be set to zero. Only relevant when <code>ls.meth = "qr"</code>
AND the predictor matrix is rank-deficient.</dd>
<dt>...</dt>
<dd>Other named arguments.</dd></dl>

Arguments

Gerko Vink, 2018, for this version, based on earlier versions written
by Stef van Buuren, Karin Groothuis-Oudshoorn, 2017

Author

Draws values of beta and sigma by Bayesian linear regression — norm.draw

<dl>

<dt>y</dt>
<dd>Incomplete data vector of length <code>n</code></dd>


<dt>ry</dt>
<dd>Vector of missing data pattern (<code>FALSE</code>=missing,
<code>TRUE</code>=observed)</dd>


<dt>x</dt>
<dd>Matrix (<code>n</code> x <code>p</code>) of complete covariates.</dd>


<dt>rank.adjust</dt>
<dd>Argument that specifies whether <code>NA</code>'s in the
coefficients need to be set to zero. Only relevant when <code>ls.meth = "qr"</code>
AND the predictor matrix is rank-deficient.</dd>


<dt>...</dt>
<dd>Other named arguments.</dd>

</dl>

Draws values of beta and sigma by Bayesian linear regression

norm.draw: Draws values of beta and sigma by Bayesian linear regression

Description

Usage

Value

Arguments

Author

References