mice.impute.lag

Function for computing the lagged values of an imputed response in <code>mice</code>.

internal

Easy-to-use and efficient interface for
Bayesian inference of complex panel (time series) data using dynamic
multivariate panel models by Helske and Tikka (2024)
<doi:10.1016/j.alcr.2024.100617>. The package supports joint modeling of
multiple measurements per individual, time-varying and time-invariant
effects, and a wide range of discrete and continuous distributions.
Estimation of these dynamic multivariate panel models is carried out via
'Stan'. For an in-depth tutorial of the package, see
(Tikka and Helske, 2024) <doi:10.48550/arXiv.2302.01607>.

Santtu Tikka

dynamite

Bayesian Modeling and Causal Inference for Multivariate
Longitudinal Data

Jouni Helske

Nicholas Clark

Lucy D'Agostino McGowan

mice.impute.lag function

<dl><dt>y</dt>
<dd>Vector to be imputed</dd>
<dt>ry</dt>
<dd>Logical vector of length <code>length(y)</code> indicating the
the subset <code>y[ry]</code> of elements in <code>y</code> to which the imputation
model is fitted. The <code>ry</code> generally distinguishes the observed
(<code>TRUE</code>) and missing values (<code>FALSE</code>) in <code>y</code>.</dd>
<dt>x</dt>
<dd>Numeric design matrix with <code>length(y)</code> rows with predictors for
<code>y</code>. Matrix <code>x</code> may have no missing values.</dd>
<dt>wy</dt>
<dd>Logical vector of length <code>length(y)</code>. A <code>TRUE</code> value
indicates locations in <code>y</code> for which imputations are created.</dd>
<dt>group_val</dt>
<dd>[<code><a href="/link/vector()?package=dynamite&version=1.5.6" data-mini-rdoc="dynamite::vector()">vector()</a></code>] Values of the grouping variable.</dd>
<dt>group_var</dt>
<dd>[<code>character(1)</code>] Name of the grouping variable.</dd>
<dt>resp</dt>
<dd>[<code>character(1)</code>] Name of the response variable.</dd>
<dt>...</dt>
<dd>Other named arguments.</dd></dl>

Arguments

Compute Lagging Values of an Imputed Response — mice.impute.lag

<dl>

<dt>y</dt>
<dd>Vector to be imputed</dd>


<dt>ry</dt>
<dd>Logical vector of length <code>length(y)</code> indicating the
the subset <code>y[ry]</code> of elements in <code>y</code> to which the imputation
model is fitted. The <code>ry</code> generally distinguishes the observed
(<code>TRUE</code>) and missing values (<code>FALSE</code>) in <code>y</code>.</dd>


<dt>x</dt>
<dd>Numeric design matrix with <code>length(y)</code> rows with predictors for
<code>y</code>. Matrix <code>x</code> may have no missing values.</dd>


<dt>wy</dt>
<dd>Logical vector of length <code>length(y)</code>. A <code>TRUE</code> value
indicates locations in <code>y</code> for which imputations are created.</dd>


<dt>group_val</dt>
<dd>[<code><a href='https://rdrr.io/r/base/vector.html'>vector()</a></code>] Values of the grouping variable.</dd>


<dt>group_var</dt>
<dd>[<code>character(1)</code>] Name of the grouping variable.</dd>


<dt>resp</dt>
<dd>[<code>character(1)</code>] Name of the response variable.</dd>


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

</dl>

mice.impute.lag: Compute Lagging Values of an Imputed Response

Description

Usage

Arguments