seqQuickLook

The <code>seqQuickLook()</code> function aimed at providing an overview of the
number and size of the different types of gaps
spread in the original dataset.

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

seqQuickLook function

<dl><dt>data</dt>
<dd>a data.frame where missing data are coded as NA or
a state sequence object built with seqdef function</dd>
<dt>var</dt>
<dd>the list of columns containing the trajectories.
Default is NULL, i.e. all the columns.</dd>
<dt>np</dt>
<dd>number of previous observations in the imputation model of the
internal gaps.</dd>
<dt>nf</dt>
<dd>number of future observations in the imputation model of the
internal gaps.</dd></dl>

Arguments

Author

Summary of the types of gaps among a dataset — seqQuickLook

<dl>

<dt>data</dt>
<dd>a data.frame where missing data are coded as NA or
a state sequence object built with seqdef function</dd>


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


<dt>np</dt>
<dd>number of previous observations in the imputation model of the
internal gaps.</dd>


<dt>nf</dt>
<dd>number of future observations in the imputation model of the
internal gaps.</dd>

</dl>

seqQuickLook: Summary of the types of gaps among a dataset

Description

Usage

Value

Arguments

Author

Details

Examples