lcmm interface
# S4 method for lcMethodLcmmGMM
getName(object)# S4 method for lcMethodLcmmGMM
getShortName(object)
# S4 method for lcMethodLcmmGMM
preFit(method, data, envir, verbose, ...)
# S4 method for lcMethodLcmmGMM
fit(method, data, envir, verbose, ...)
# S4 method for lcMethodLcmmGBTM
getName(object)
# S4 method for lcMethodLcmmGBTM
getShortName(object)
# S4 method for lcMethodLcmmGBTM
preFit(method, data, envir, verbose, ...)
# S4 method for lcMethodLcmmGBTM
fit(method, data, envir, verbose, ...)
# S3 method for lcModelLcmmGMM
fitted(object, ..., clusters = trajectoryAssignments(object))
# S3 method for lcModelLcmmGMM
predict(object, ..., newdata = NULL, what = "mu")
# S3 method for lcModelLcmmGMM
model.matrix(object, ..., what = "mu")
# S3 method for lcModelLcmmGMM
logLik(object, ...)
# S3 method for lcModelLcmmGMM
sigma(object, ...)
# S4 method for lcModelLcmmGMM
postprob(object, ...)
# S4 method for lcModelLcmmGMM
converged(object, ...)
The object to extract the label from.
The lcMethod object.
The data, as a data.frame, on which the model will be trained.
The environment in which the lcMethod should be evaluated
A R.utils::Verbose object indicating the level of verbosity.
Additional arguments.
Optional cluster assignments per id. If unspecified, a matrix is returned containing the cluster-specific predictions per column.
Optional data frame for which to compute the model predictions. If omitted, the model training data is used. Cluster trajectory predictions are made when ids are not specified. If the clusters are specified under the Cluster column, output is given only for the specified cluster. Otherwise, a matrix is returned with predictions for all clusters.
The distributional parameter to predict. By default, the mean response 'mu' is predicted. The cluster membership predictions can be obtained by specifying what='mb'.