lcModel {latrend} | R Documentation |
Longitudinal cluster result (lcModel
)
Description
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()
: Adata.frame
containing the longitudinal representation of each cluster. -
plotClusterTrajectories()
: Plots the longitudinal representation of each cluster. -
fittedTrajectories()
: Adata.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 secondlcModel
. -
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
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)
}