The latrend package provides a framework for clustering longitudinal datasets in a standardized way. It provides interfaces to various R packages for longitudinal clustering.
- Unified cluster analysis, independent of the underlying algorithms used. Enabling users to compare the performance of various longitudinal cluster methods on the case study at hand.
- Supports many different methods for longitudinal clustering out of the box (see the list of supported packages below).
- The framework consists of extensible
S4methods based on an abstract model class, enabling rapid prototyping of new cluster methods or model specifications. - Standard plotting tools for model evaluation across methods (e.g., trajectories, cluster trajectories, model fit, metrics)
- Support for many cluster metrics through the packages clusterCrit, mclustcomp, and igraph.
- The structured and unified analysis approach enables simulation studies for comparing methods.
- Standardized model validation for all methods through bootstrapping or k-fold cross-validation.
remotes::install_github("https://github.com/philips-software/latrend")
Including vignettes:
remotes::install_github("https://github.com/philips-software/latrend", build_vignettes = TRUE)
library(latrend)
Load and view example data.
data(latrendData)
head(latrendData)
options(latrend.id = "Id", latrend.time = "Time")
plotTrajectories(latrendData, response = "Y")
Cluster the trajectories and plot the results.
kmlMethod <- lcMethodKML("Y", nClusters = 3)
model <- latrend(kmlMethod, data = latrendData)
summary(model)
plot(model)
The latrend package provides interfaces to the relevant methods for longitudinal clustering for the following packages: