predictPostprob: Posterior probability for new data

In philips-software/latrend: A Framework for Clustering Longitudinal Data

Posterior probability for new data

Description

Returns the observation-specific posterior probabilities for the given data.

For lcModel: The default implementation returns a uniform probability matrix.

Usage

predictPostprob(object, newdata = NULL, ...)

## S4 method for signature 'lcModel'
predictPostprob(object, newdata = NULL, ...)

Arguments

object	The model.
newdata	Optional data.frame for which to compute the posterior probability. If omitted, the model training data is used.
...	Additional arguments passed to postprob.

Value

A N-by-K matrix indicating the posterior probability per trajectory per measurement on each row, for each cluster (the columns). Here, N = nrow(newdata) and K = nClusters(object).

Implementation

Classes extending lcModel should override this method to enable posterior probability predictions for new data.

setMethod("predictPostprob", "lcModelExt", function(object, newdata = NULL, ...) {
  # return observation-specific posterior probability matrix
})

See Also

postprob

Other lcModel functions: clusterNames(), clusterProportions(), clusterSizes(), clusterTrajectories(), coef.lcModel(), converged(), deviance.lcModel(), df.residual.lcModel(), estimationTime(), externalMetric(), fitted.lcModel(), fittedTrajectories(), getCall.lcModel(), getLcMethod(), ids(), lcModel-class, metric(), model.frame.lcModel(), nClusters(), nIds(), nobs.lcModel(), plot-lcModel-method, plotClusterTrajectories(), plotFittedTrajectories(), postprob(), predict.lcModel(), predictAssignments(), predictForCluster(), qqPlot(), residuals.lcModel(), sigma.lcModel(), strip(), time.lcModel(), trajectoryAssignments()

philips-software/latrend documentation built on July 16, 2025, 5:43 p.m.