Imputation of Missing Data in Sequence Analysis

Documentation for package ‘seqimpute’ version 2.0

addcluster	Function that adds the clustering result to an imputed dataset obtained with seqimpute
CO	Dataset containing 3 fixed covariates about the game addiction of young subjects
COt	Dataset containing 1 time-dependant covariate about the game addiction of young subjects
OD	Dataset containing variables about the game addiction of young subjects
onlyimputed	Extract only the completed datasets from the results obtained with seqimpute function. Therefore, the original dataset with missing values is discarded, together with the two first columns (".id" and ".imp")".
seqaddNA	Generation of missing data under the form of gaps, which is the typical form of missing data with longitudinal data. A missing completely at random (MAR) mechanism is used.
seqcomplete	Extract all the sequences without missing value.
seqimpute	Imputation of missing data in sequence analysis
seqmissfplot	Plot of the most common patterns of missing data.
seqmissimplic	Function built on the seqimplic function of the TraMineRextras package. Visualization and identification of the states that best characterize sequence with missing data vs the sequences without missing data at each position (time point). See the seqimplic helps to more details on how it works.
seqmissIplot	Plot all the patterns of missing data.
seqQuickLook	Numbering NAs and types of gaps among a dataset
seqTrans	Computing and spotting transitions among a dataset
seqwithmiss	Extract all the sequences with at least one missing value