Imputation of Missing Data in Sequence Analysis
Function that adds the clustering result to a seqimp object obtained...
Transform an object of class seqimp into a dataframe or a midsobje...
Plot a seqimp object
Print a seqimp object
Generation of missing on longitudinal categorical data.
Extract all the trajectories without missing value.
seqimpute: Imputation of missing data in longitudinal categorical data
Plot the most common patterns of missing data.
Identification and visualization of states that best characterize sequ...
Plot all the patterns of missing data.
Summary of the types of gaps among a dataset
Spotting impossible transitions in longitudinal categorical data
Extract all the trajectories with at least one missing value
Summary of a seqimp 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.