summary.seqimp

Summary of a <code>seqimp</code> object

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

summary.seqimp function

<dl><dt>object</dt>
<dd>of class <code>seqimp</code></dd>
<dt>...</dt>
<dd>additional arguments passed to other functions</dd></dl>

Arguments

Author

Summary of a seqimp object — summary.seqimp

<dl>

<dt>object</dt>
<dd>of class <code>seqimp</code></dd>


<dt>...</dt>
<dd>additional arguments passed to other functions</dd>

</dl>

summary.seqimp: Summary of a `seqimp` object

Description

Usage

Arguments

Author