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.
| Method | Description | Source |
| lcMethodAkmedoids | Anchored k-medoids adepeju2020akmedoidslatrend | akmedoids |
| lcMethodCrimCV | Group-based trajectory modeling of count data nielsen2018crimcvlatrend | crimCV |
| lcMethodDtwclust | Methods for distance-based clustering, including dynamic time warping sardaespinosa2019timelatrend | dtwclust |
| lcMethodFeature | Feature-based clustering | |
| lcMethodFlexmix | Interface to the FlexMix framework gruen2008flexmixlatrend | flexmix |
| lcMethodFlexmixGBTM | Group-based trajectory modeling | flexmix |
| lcMethodFunFEM | Model-based clustering using funFEM bouveyron2015funfemlatrend | funFEM |
| lcMethodGCKM | Growth-curve modeling and k-means | lme4 |
| lcMethodKML | Longitudinal k-means genolini2015kmllatrend | kml |
| lcMethodLcmmGBTM | Group-based trajectory modeling proustlima2017estimationlatrend | lcmm |
| lcMethodLcmmGMM | Growth mixture modeling proustlima2017estimationlatrend | lcmm |
| lcMethodLMKM | Feature-based clustering using linear regression and k-means | |
| lcMethodMclustLLPA | Longitudinal latent profile analysis scrucca2016mclustlatrend | mclust |
| lcMethodMixAK_GLMM | Mixture of generalized linear mixed models | mixAK |
| lcMethodMixtoolsGMM | Growth mixture modeling | mixtools |
| lcMethodMixtoolsNPRM | Non-parametric repeated measures clustering benaglia2009mixtoolslatrend | 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.
data(latrendData)
method <- lcMethodLMKM(Y ~ Time, id = "Id", time = "Time")
model <- latrend(method, data = latrendData)