seqcomplete

Extract all the trajectories without missing value.

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

seqcomplete function

<dl><dt>data</dt>
<dd>either a data frame containing sequences of a multinomial
variable with missing data (coded as <code>NA</code>) or a state sequence
object built with the TraMineR package</dd>
<dt>var</dt>
<dd>the list of columns containing the trajectories.
Default is NULL, i.e. all the columns.</dd></dl>

Arguments

Author

Extract all the trajectories without missing value. — seqcomplete

<dl>

<dt>data</dt>
<dd>either a data frame containing sequences of a multinomial
variable with missing data (coded as <code>NA</code>) or a state sequence
object built with the TraMineR package</dd>


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

</dl>

seqcomplete: Extract all the trajectories without missing value.

Description

Usage

Value

Arguments

Author

Examples