funFEM interface
# S4 method for lcMethodFunFEM
getArgumentDefaults(object)# S4 method for lcMethodFunFEM
getArgumentExclusions(object)
# S4 method for lcMethodFunFEM
getName(object)
# S4 method for lcMethodFunFEM
getShortName(object)
# S4 method for lcMethodFunFEM
preFit(method, data, envir, verbose, ...)
# S4 method for lcMethodFunFEM
fit(method, data, envir, verbose, ...)
# S3 method for lcModelFunFEM
fitted(object, ..., clusters = trajectoryAssignments(object))
# S4 method for lcModelFunFEM
predictForCluster(
object,
newdata,
cluster,
what = "mu",
approxFun = approx,
...
)
# S4 method for lcModelFunFEM
postprob(object, ...)
# S3 method for lcModelFunFEM
coef(object, ...)
# S3 method for lcModelFunFEM
logLik(object, ...)
# S4 method for lcModelFunFEM
converged(object, ...)
The 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().
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.
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'.
Function to interpolate between measurement moments, approx() by default.