print.seqimp

Print 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

print.seqimp function

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

Arguments

Author

Print a seqimp object — print.seqimp

<dl>

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


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

</dl>

print.seqimp: Print a `seqimp` object

Description

Usage

Arguments

Author