crimCV interface
# S4 method for lcMethodCrimCV
getName(object)# S4 method for lcMethodCrimCV
getShortName(object)
# S4 method for lcMethodCrimCV
prepareData(method, data, verbose, ...)
# S4 method for lcMethodCrimCV
fit(method, data, envir, verbose, ...)
# S4 method for lcModelCrimCV
postprob(object)
# S3 method for lcModelCrimCV
predict(object, ..., newdata = NULL, what = "mean")
# S3 method for lcModelCrimCV
fitted(object, ..., clusters = trajectoryAssignments(object), what = "mean")
# S3 method for lcModelCrimCV
logLik(object, ...)
# S3 method for lcModelCrimCV
coef(object, ...)
# S4 method for lcModelCrimCV
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.
A R.utils::Verbose object indicating the level of verbosity.
Additional arguments.
The environment in which the lcMethod should be evaluated
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'.
Optional cluster assignments per id. If unspecified, a matrix is returned containing the cluster-specific predictions per column.