Returns the observation-specific posterior probabilities for the given data.
For lcModel: The default implementation returns a uniform probability matrix.
predictPostprob(object, newdata = NULL, ...)# S4 method for lcModel
predictPostprob(object, newdata = NULL, ...)
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).
The model.
Optional data.frame for which to compute the posterior probability. If omitted, the model training data is used.
Additional arguments passed to postprob.
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
})
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()