plot.seqimp

Plot a <code>seqimp</code> object. The state distribution plot of the first
<code>m</code> completed datasets is shown, possibly alongside the original
dataset with missing data

Multiple imputation of missing data in a dataset using MICT or
MICT-timing methods. The core idea of the algorithms is to fill gaps of
missing data, which is the typical form of missing data in a longitudinal
setting, recursively from their edges. Prediction is based on either a
multinomial or random forest regression model. Covariates and
time-dependent covariates can be included in the model.

Kevin Emery

seqimpute

Imputation of Missing Data in Sequence Analysis

Anthony Guinchard

Andre Berchtold

Kamyar Taher

plot.seqimp function

<dl><dt>x</dt>
<dd>Object of class <code>seqimp</code></dd>
<dt>m</dt>
<dd>Number of completed datasets to show</dd>
<dt>include</dt>
<dd>logical that indicates if the original dataset with missing
value should be plotted or not</dd>
<dt>...</dt>
<dd>Arguments to be passed to the seqdplot function</dd></dl>

Arguments

Author

Plot a seqimp object — plot.seqimp

<dl>

<dt>x</dt>
<dd>Object of class <code>seqimp</code></dd>


<dt>m</dt>
<dd>Number of completed datasets to show</dd>


<dt>include</dt>
<dd>logical that indicates if the original dataset with missing
value should be plotted or not</dd>


<dt>...</dt>
<dd>Arguments to be passed to the seqdplot function</dd>

</dl>

plot.seqimp: Plot a `seqimp` object

Description

Usage

Arguments

Author