Longitudinal cluster result (lcModel)

A longitudinal cluster model ([lcModel][lcModel-class]) describes the clustered representation of a certain longitudinal dataset.

A lcModel is obtained by estimating a specified longitudinal cluster method on a longitudinal dataset. The estimation is done via one of the latrend estimation functions.

A longitudinal cluster result represents the dataset in terms of a partitioning of the trajectories into a number of clusters. The trajectoryAssignments() function outputs the most likely membership for the respective trajectories. Each cluster has a longitudinal representation, obtained via clusterTrajectories(), and can be plotted via plotClusterTrajectories().

Functionality

Clusters and partitioning:

• nClusters(): The number of clusters this model represents.

• clusterNames(): The names of the clusters.

• clusterSizes(): The respective number of trajectories assigned to each cluster.

• clusterProportions(): The respective proportional size of each cluster.

• trajectoryAssignments(): The most likely cluster membership of each trajectory.

• postprob(): The posterior probability of each trajectory to each cluster.

Longitudinal cluster representation (i.e., trends):

• clusterTrajectories(): A data.frame containing the longitudinal representation of each cluster.

• plotClusterTrajectories(): Plots the longitudinal representation of each cluster.

• fittedTrajectories(): A data.frame containing the longitudinal representation of each trajectory. For many methods, this is the cluster center.

• plotFittedTrajectories(): Plot the trajectory representation.

Training data:

• nIds(): The number of trajectories used for estimation.

• ids(): A vector of identifiers of the trajectories that were used for estimation.

• nobs(): The number of observations used for estimation, across trajectories.

• time(): Moments in time on which observations are present.

• trajectories(): The trajectories that were used for estimation.

• plotTrajectories(): Plot the trajectories that were used for estimation.

Model evaluation:

• summary(): Obtain a summary of the model.

• metric(): Compute an internal metric.

• externalMetric(): Compute an external metric in relation to a second lcModel.

• converged(): Whether the estimation procedure converged.

• estimationTime(): Total time that was needed for the fitting steps.

• sigma(): Residual error scale.

• qqPlot(): QQ plot of the model residuals.

Model prediction:

• predictForCluster(): Cluster-specific prediction on new data. Not supported for all methods.

• predictPostprob(): Predict posterior probability for new data. Not supported for all methods.

• predictAssignments(): Predict cluster membership for new data. Not supported for all methods.

Other functionality:

• getLcMethod(): Get the method specification by which this model was estimated.

• update(): Retrain a model with altered method arguments.

• strip(): Removes non-essential (meta) data and environments from the model to facilitate efficient serialization.

See also

lcModel

Examples

data(latrendData)
# define the method
method <- lcMethodLMKM(Y ~ Time, id = "Id", time = "Time")
# estimate the method, giving the model
model <- latrend(method, data = latrendData)

if (require("ggplot2")) {
  plotClusterTrajectories(model)
}