seqTrans

The purpose of <code>seqTrans</code> is to spot impossible transitions
in longitudinal categorical 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

seqTrans function

<dl><dt>data</dt>
<dd>a data frame containing sequences of a multinomial
variable with missing data (coded as <code>NA</code>)</dd>
<dt>var</dt>
<dd>the list of columns containing the trajectories.
Default is NULL, i.e. all the columns.</dd>
<dt>trans</dt>
<dd><code>character</code> vector gathering the impossible transitions.
For example: trans &lt;- c("1-&gt;3","1-&gt;4","2-&gt;1","4-&gt;1","4-&gt;3")</dd></dl>

Arguments

Author

Spotting impossible transitions in longitudinal categorical data — seqTrans

<dl>

<dt>data</dt>
<dd>a data frame containing sequences of a multinomial
variable with missing data (coded as <code>NA</code>)</dd>


<dt>var</dt>
<dd>the list of columns containing the trajectories.
Default is NULL, i.e. all the columns.</dd>


<dt>trans</dt>
<dd><code>character</code> vector gathering the impossible transitions.
For example: trans &lt;- c("1-&gt;3","1-&gt;4","2-&gt;1","4-&gt;1","4-&gt;3")</dd>

</dl>

seqTrans: Spotting impossible transitions in longitudinal categorical data

Description

Usage

Value

Arguments

Author

Examples