lcmm interface
# S4 method for lcMethodLcmmGMM
getArgumentDefaults(object)# S4 method for lcMethodLcmmGMM
getArgumentExclusions(object)
# S4 method for lcMethodLcmmGMM
getName(object)
# S4 method for lcMethodLcmmGMM
getShortName(object)
# S4 method for lcMethodLcmmGMM
validate(method, data, envir = NULL, ...)
# S4 method for lcMethodLcmmGMM
preFit(method, data, envir, verbose, ...)
# S4 method for lcMethodLcmmGMM
fit(method, data, envir, verbose, ...)
# S4 method for lcMethodLcmmGMM
responseVariable(object, ...)
# S4 method for lcMethodLcmmGBTM
getArgumentDefaults(object)
# S4 method for lcMethodLcmmGBTM
getArgumentExclusions(object)
# 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, ...)
# S4 method for lcMethodLcmmGBTM
responseVariable(object, ...)
# S3 method for lcModelLcmmGMM
fitted(object, ..., clusters = trajectoryAssignments(object))
# S4 method for lcModelLcmmGMM
predictForCluster(object, newdata, cluster, 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 lcMethod or lcModel object.
An object inheriting from lcMethod with all its arguments having been evaluated and finalized.
A data.frame representing the transformed training data.
The environment containing variables generated by prepareData() and preFit().
Additional arguments.
A R.utils::Verbose object indicating the level of verbosity.
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.
The cluster name (as character) to predict for.
The distributional parameter to predict. By default, the mean response 'mu' is predicted. The cluster membership predictions can be obtained by specifying what = 'mb'.