latrend-methods: Supported methods for longitudinal clustering

In latrend: A Framework for Clustering Longitudinal Data

Supported methods for longitudinal clustering

Description

This page provides an overview of the currently supported methods for longitudinal clustering. For general recommendations on which method to apply to your dataset, see here.

Supported methods

Method	Description	Source
lcMethodAkmedoids	Anchored k-medoids \insertCiteadepeju2020akmedoidslatrend	akmedoids
lcMethodCrimCV	Group-based trajectory modeling of count data \insertCitenielsen2018crimcvlatrend	crimCV
lcMethodDtwclust	Methods for distance-based clustering, including dynamic time warping \insertCitesardaespinosa2019timelatrend	dtwclust
lcMethodFeature	Feature-based clustering
lcMethodFlexmix	Interface to the FlexMix framework \insertCitegruen2008flexmixlatrend	flexmix
lcMethodFlexmixGBTM	Group-based trajectory modeling	flexmix
lcMethodFunFEM	Model-based clustering using funFEM \insertCitebouveyron2015funfemlatrend	funFEM
lcMethodGCKM	Growth-curve modeling and k-means	lme4
lcMethodKML	Longitudinal k-means \insertCitegenolini2015kmllatrend	kml
lcMethodLcmmGBTM	Group-based trajectory modeling \insertCiteproustlima2017estimationlatrend	lcmm
lcMethodLcmmGMM	Growth mixture modeling \insertCiteproustlima2017estimationlatrend	lcmm
lcMethodLMKM	Feature-based clustering using linear regression and k-means
lcMethodMclustLLPA	Longitudinal latent profile analysis \insertCitescrucca2016mclustlatrend	mclust
lcMethodMixAK_GLMM	Mixture of generalized linear mixed models	mixAK
lcMethodMixtoolsGMM	Growth mixture modeling	mixtools
lcMethodMixtoolsNPRM	Non-parametric repeated measures clustering \insertCitebenaglia2009mixtoolslatrend	mixtools
lcMethodMixTVEM	Mixture of time-varying effects models
lcMethodRandom	Random partitioning
lcMethodStratify	Stratification rule

In addition, the functionality of any method can be extended via meta methods. This is used for extending the estimation procedure of a method, such as repeated fitting and selecting the best result, or fitting until convergence.

It is strongly encouraged to evaluate and compare several candidate methods in order to identify the most suitable method.

References

\insertAllCited

See Also

latrend-approaches latrend-estimation latrend-metrics

Examples

data(latrendData)
method <- lcMethodLMKM(Y ~ Time, id = "Id", time = "Time")
model <- latrend(method, data = latrendData)

latrend documentation built on March 31, 2023, 5:45 p.m.