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 (Adepeju et al. 2020) | akmedoids |
| lcMethodCrimCV | Group-based trajectory modeling of count data (Nielsen 2018) | crimCV |
| lcMethodDtwclust | Methods for distance-based clustering, including dynamic time warping (Sardá-Espinosa 2019) | dtwclust |
| lcMethodFeature | Feature-based clustering | |
| lcMethodFlexmix | Interface to the FlexMix framework (Grün and Leisch 2008) | flexmix |
| lcMethodFlexmixGBTM | Group-based trajectory modeling | flexmix |
| lcMethodFunFEM | Model-based clustering using funFEM (Bouveyron 2015) | funFEM |
| lcMethodGCKM | Growth-curve modeling and k-means | lme4 |
| lcMethodKML | Longitudinal k-means (Genolini et al. 2015) | kml |
| lcMethodLcmmGBTM | Group-based trajectory modeling (Proust-Lima et al. 2017) | lcmm |
| lcMethodLcmmGMM | Growth mixture modeling (Proust-Lima et al. 2017) | lcmm |
| lcMethodLMKM | Feature-based clustering using linear regression and k-means | |
| lcMethodMclustLLPA | Longitudinal latent profile analysis (Scrucca et al. 2016) | mclust |
| lcMethodMixAK_GLMM | Mixture of generalized linear mixed models | mixAK |
| lcMethodMixtoolsGMM | Growth mixture modeling | mixtools |
| lcMethodMixtoolsNPRM | Non-parametric repeated measures clustering (Benaglia et al. 2009) | 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.
Adepeju M, Langton S, Bannister J (2020).
akmedoids: Anchored Kmedoids for Longitudinal Data Clustering.
R package version 0.1.5, https://CRAN.R-project.org/package=akmedoids.
Benaglia T, Chauveau D, Hunter DR, Young D (2009).
“mixtools: An R Package for Analyzing Finite Mixture Models.”
Journal of Statistical Software, 32(6), 1--29.
doi:10.18637/jss.v032.i06
.
Bouveyron C (2015).
funFEM: Clustering in the Discriminative Functional Subspace.
R package version 1.1, https://CRAN.R-project.org/package=funFEM.
Genolini C, Alacoque X, Sentenac M, Arnaud C (2015).
“kml and kml3d: R Packages to Cluster Longitudinal Data.”
Journal of Statistical Software, 65(4), 1--34.
doi:10.18637/jss.v065.i04
.
Grün B, Leisch F (2008).
“FlexMix Version 2: Finite Mixtures with Concomitant Variables and Varying and Constant Parameters.”
Journal of Statistical Software, 28(4), 1--35.
doi:10.18637/jss.v028.i04
.
Nielsen JD (2018).
crimCV: Group-Based Modelling of Longitudinal Data.
R package version 0.9.6, https://CRAN.R-project.org/package=crimCV.
Proust-Lima C, Philipps V, Liquet B (2017).
“Estimation of Extended Mixed Models Using Latent Classes and Latent Processes: The R Package lcmm.”
Journal of Statistical Software, 78(2), 1--56.
doi:10.18637/jss.v078.i02
.
Sardá-Espinosa A (2019).
“Time-Series Clustering in R Using the dtwclust Package.”
The R Journal.
doi:10.32614/RJ-2019-023
.
Scrucca L, Fop M, Murphy TB, Raftery AE (2016).
“mclust 5: clustering, classification and density estimation using Gaussian finite mixture models.”
The R Journal, 8(1), 205--233.
data(latrendData)
method <- lcMethodLMKM(Y ~ Time, id = "Id", time = "Time")
model <- latrend(method, data = latrendData)