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, ...)The model.
Optional data.frame for which to compute the posterior probability. If omitted, the model training data is used.
Arguments passed on to postprob
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).
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
})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()