Function reference • latrend

All functions
`APPA()`	Average posterior probability of assignment (APPA)
`OCC()`	Odds of correct classification (OCC)
`PAP.adh`	Weekly Mean PAP Therapy Usage of OSA Patients in the First 3 Months
`PAP.adh1y`	Biweekly Mean PAP Therapy Adherence of OSA Patients over 1 Year
`as.data.frame(<lcMethod>)`	Convert lcMethod arguments to a list of atomic types
`as.data.frame(<lcMethods>)`	Convert a list of lcMethod objects to a data.frame
`as.data.frame(<lcModels>)`	Generate a data.frame containing the argument values per method per row
`as.lcMethods()`	Convert a list of lcMethod objects to a lcMethods list
`as.lcModels()`	Convert a list of lcModels to a lcModels list
`as.list(<lcMethod>)`	Extract the method arguments as a list
`clusterNames<-`()	Update the cluster names
`clusterNames()`	Get the cluster names
`clusterProportions()`	Proportional size of each cluster
`clusterSizes()`	Number of trajectories per cluster
`clusterTrajectories()`	Extract cluster trajectories
`coef(<lcModel>)`	Extract lcModel coefficients
`compose()`	`lcMethod` estimation step: compose an lcMethod object
`confusionMatrix()`	Compute the posterior confusion matrix
`converged()`	Check model convergence
`createTestDataFold()`	Create the test fold data for validation
`createTestDataFolds()`	Create all k test folds from the training data
`createTrainDataFolds()`	Create the training data for each of the k models in k-fold cross validation evaluation
`defineExternalMetric()`	Define an external metric for lcModels
`defineInternalMetric()`	Define an internal metric for lcModels
`deviance(<lcModel>)`	lcModel deviance
`df.residual(<lcModel>)`	Extract the residual degrees of freedom from a lcModel
`estimationTime()`	Estimation time
`evaluate(<lcMethod>)`	Substitute the call arguments for their evaluated values
`externalMetric(<lcModel>,<lcModel>)` `externalMetric(<lcModels>,<missing>)` `externalMetric(<lcModels>,<character>)` `externalMetric(<lcModels>,<lcModel>)` `externalMetric(<list>,<lcModel>)`	Compute external model metric(s)
`fit()`	`lcMethod` estimation step: logic for fitting the method to the processed data
`fitted(<lcModel>)`	Extract lcModel fitted values
`fittedTrajectories()`	Extract the fitted trajectories
`formula(<lcMethod>)`	Extract formula
`formula(<lcModel>)`	Extract the formula of a lcModel
`generateLongData()`	Generate longitudinal test data
`getArgumentDefaults()`	Default argument values for the given method specification
`getArgumentExclusions()`	Arguments to be excluded from the specification
`getCitation()`	Get citation info
`getExternalMetricDefinition()`	Get the external metric definition
`getExternalMetricNames()`	Get the names of the available external metrics
`getInternalMetricDefinition()`	Get the internal metric definition
`getInternalMetricNames()`	Get the names of the available internal metrics
`getLabel()`	Object label
`getLcMethod()`	Get the method specification
`getName()` `getShortName()`	Object name
`idVariable()`	Extract the trajectory identifier variable
`ids()`	Get the trajectory ids on which the model was fitted
`$`(<lcMethod>) `[[`(<lcMethod>)	Retrieve and evaluate a lcMethod argument by name
`initialize(<lcMethod>)`	lcMethod initialization
`compose(<lcMetaMethod>)` `getLcMethod(<lcMetaMethod>)` `getName(<lcMetaMethod>)` `getShortName(<lcMetaMethod>)` `idVariable(<lcMetaMethod>)` `preFit(<lcMetaMethod>)` `prepareData(<lcMetaMethod>)` `fit(<lcMetaMethod>)` `postFit(<lcMetaMethod>)` `responseVariable(<lcMetaMethod>)` `timeVariable(<lcMetaMethod>)` `validate(<lcMetaMethod>)` `update(<lcMetaMethod>)` `fit(<lcFitConverged>)` `validate(<lcFitConverged>)` `fit(<lcFitRep>)` `validate(<lcFitRep>)`	lcMetaMethod abstract class
`latrend-approaches`	High-level approaches to longitudinal clustering
`latrend-data`	Longitudinal dataset representation
`latrend-estimation`	Overview of `lcMethod` estimation functions
`latrend-generics`	Generics used by latrend for different classes
`latrend-methods`	Supported methods for longitudinal clustering
`latrend-metrics`	Metrics
`latrend-package`	latrend: A Framework for Clustering Longitudinal Data
`latrend-parallel`	Parallel computation using latrend
`latrend()`	Cluster longitudinal data using the specified method
`latrendBatch()`	Cluster longitudinal data for a list of method specifications
`latrendBoot()`	Cluster longitudinal data using bootstrapping
`latrendCV()`	Cluster longitudinal data over k folds
`latrendData`	Artificial longitudinal dataset comprising three classes
`latrendRep()`	Cluster longitudinal data repeatedly
`fitted(<lcApproxModel>)` `predictForCluster(<lcApproxModel>)`	lcApproxModel class
`lcFitConverged()` `lcFitRep()` `lcFitRepMin()` `lcFitRepMax()`	Method fit modifiers
`lcMethod-class` `lcMethod`	lcMethod class
`lcMethod-estimation` `latrend-procedure` `lcMethod-steps`	Longitudinal cluster method (`lcMethod`) estimation procedure
`lcMethodAkmedoids()`	Specify AKMedoids method
`lcMethodCrimCV()`	Specify a zero-inflated repeated-measures GBTM method
`lcMethodDtwclust()`	Specify time series clustering via dtwclust
`lcMethodFeature()`	Feature-based clustering
`lcMethodFlexmix()`	Method interface to flexmix()
`lcMethodFlexmixGBTM()`	Group-based trajectory modeling using flexmix
`lcMethodFunFEM()`	Specify a FunFEM method
`lcMethodFunction()`	Specify a custom method based on a function
`lcMethodGCKM()`	Two-step clustering through latent growth curve modeling and k-means
`lcMethodKML()`	Specify a longitudinal k-means (KML) method
`lcMethodLMKM()`	Two-step clustering through linear regression modeling and k-means
`lcMethodLcmmGBTM()`	Specify GBTM method
`lcMethodLcmmGMM()`	Specify GMM method using lcmm
`lcMethodMclustLLPA()`	Longitudinal latent profile analysis
`lcMethodMixAK_GLMM()`	Specify a GLMM iwht a normal mixture in the random effects
`lcMethodMixTVEM()`	Specify a MixTVEM
`lcMethodMixtoolsGMM()`	Specify mixed mixture regression model using mixtools
`lcMethodMixtoolsNPRM()`	Specify non-parametric estimation for independent repeated measures
`lcMethodRandom()`	Specify a random-partitioning method
`lcMethodStratify()`	Specify a stratification method
`lcMethods()`	Generate a list of lcMethod objects
`lcModel-class`	`lcModel` class
`lcModel`	Longitudinal cluster result (`lcModel`)
`lcModelPartition()`	Create a lcModel with pre-defined partitioning
`lcModelWeightedPartition()`	Create a lcModel with pre-defined weighted partitioning
`lcModels-class`	`lcModels`: a list of `lcModel` objects
`lcModels()`	Construct a list of `lcModel` objects
`logLik(<lcModel>)`	Extract the log-likelihood of a lcModel
`max(<lcModels>)`	Select the lcModel with the highest metric value
`metric()`	Compute internal model metric(s)
`min(<lcModels>)`	Select the lcModel with the lowest metric value
`model.data(<lcModel>)`	Extract the model data that was used for fitting
`model.frame(<lcModel>)`	Extract model training data
`nClusters()`	Number of clusters
`nIds()`	Number of trajectories
`length(<lcMethod>)` `names(<lcMethod>)`	lcMethod argument names
`nobs(<lcModel>)`	Number of observations used for the lcModel fit
`plot(<lcModel>)`	Plot a lcModel
`plot(<lcModels>)`	Grid plot for a list of models
`plotClusterTrajectories()`	Plot cluster trajectories
`plotFittedTrajectories()`	Plot the fitted trajectories
`plotMetric()`	Plot one or more internal metrics for all lcModels
`plotTrajectories()`	Plot the data trajectories
`postFit()`	`lcMethod` estimation step: logic for post-processing the fitted lcModel
`postprob()`	Posterior probability per fitted trajectory
`postprobFromAssignments()`	Create a posterior probability matrix from a vector of cluster assignments.
`preFit()`	`lcMethod` estimation step: method preparation logic
`predict(<lcModel>)`	lcModel predictions
`predictAssignments()`	Predict the cluster assignments for new trajectories
`predictForCluster()`	Predict trajectories conditional on cluster membership
`predictPostprob()`	Posterior probability for new data
`prepareData()`	`lcMethod` estimation step: logic for preparing the training data
`print(<lcMethod>)`	Print the arguments of an lcMethod object
`print(<lcModels>)`	Print lcModels list concisely
`qqPlot()`	Quantile-quantile plot
`residuals(<lcModel>)`	Extract lcModel residuals
`responseVariable()`	Extract response variable
`sigma(<lcModel>)`	Extract residual standard deviation from a lcModel
`strip()`	Reduce the memory footprint of an object for serialization
`subset(<lcModels>)`	Subsetting a lcModels list based on method arguments
`summary(<lcModel>)`	Summarize a lcModel
`test.latrend()`	Test the implementation of an lcMethod and associated lcModel subclasses
`time(<lcModel>)`	Sampling times of a lcModel
`timeVariable()`	Extract the time variable
`trajectories()`	Extract the trajectories
`trajectoryAssignments()`	Get the cluster membership of each trajectory
`transformFitted()`	Helper function for custom lcModel classes implementing fitted.lcModel()
`transformPredict()`	Helper function for custom lcModel classes implementing predict.lcModel()
`tsframe()` `meltRepeatedMeasures()`	Convert a multiple time series matrix to a data.frame
`tsmatrix()` `dcastRepeatedMeasures()`	Convert a longitudinal data.frame to a matrix
`update(<lcMethod>)`	Update a method specification
`update(<lcModel>)`	Update a lcModel
`validate()`	`lcMethod` estimation step: method argument validation logic
`which.weight()`	Sample an index of a vector weighted by the elements

Reference

All functions