longclust interface
# S4 method for lcMethodLongclust
getName(object)# S4 method for lcMethodLongclust
getShortName(object)
# S4 method for lcMethodLongclust
fit(method, data, envir, verbose, ...)
# S3 method for lcModelLongclust
predict(object, ..., newdata = NULL, what = "mu", approxFun = approx)
# S3 method for lcModelLongclust
fitted(object, ..., clusters = trajectoryAssignments(object))
# S4 method for lcModelLongclust
postprob(object, ...)
# S4 method for lcModelLongclust
converged(object, ...)
# S3 method for lcModelLongclust
logLik(object, ...)
# S3 method for lcModelLongclust
BIC(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 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'.
The interpolation function to use for time points not in the feature set.
Optional cluster assignments per id. If unspecified, a matrix is returned containing the cluster-specific predictions per column.