addcluster

Function that adds the clustering result to a <code>seqimp</code> object
obtained with the <code>seqimpute</code> function

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

addcluster function

<dl><dt>impdata</dt>
<dd>An object of class <code>seqimp</code> as created by the
<code>seqimpute</code> function</dd>
<dt>clustering</dt>
<dd>clustering made on the multiple imputed dataset. Can
either be a dataframe or a matrix, where each row correspond to an
observation and each column to a multiple imputed dataset</dd></dl>

Arguments

Function that adds the clustering result to a seqimp object
obtained with the seqimpute function — addcluster

<dl>

<dt>impdata</dt>
<dd>An object of class <code>seqimp</code> as created by the
<code>seqimpute</code> function</dd>


<dt>clustering</dt>
<dd>clustering made on the multiple imputed dataset. Can
either be a dataframe or a matrix, where each row correspond to an
observation and each column to a multiple imputed dataset</dd>

</dl>

addcluster: Function that adds the clustering result to a `seqimp` object obtained with the `seqimpute` function

Description

Usage

Value

Arguments