R: A Framework for Clustering Longitudinal Data

# A Framework for Clustering Longitudinal Data

## Documentation for package ‘latrend’ version 1.6.1

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

A C D E F G I L M N O P Q R S T U V W misc

latrend-package | latrend: A Framework for Clustering Longitudinal Data

## -- A --

APPA | Average posterior probability of assignment (APPA)
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

## -- C --

clusterNames | Get the cluster names
clusterNames<- | Update the cluster names
clusterProportions | Proportional size of each cluster
clusterProportions-method | Proportional size of each cluster
clusterSizes | Number of trajectories per cluster
clusterTrajectories | Extract cluster trajectories
clusterTrajectories-method | Extract cluster trajectories
coef.lcModel | Extract lcModel coefficients
compose | 'lcMethod' estimation step: compose an lcMethod object
compose-method | 'lcMethod' estimation step: compose an lcMethod object
compose-method | lcMetaMethod abstract class
confusionMatrix | Compute the posterior confusion matrix
converged | Check model convergence
converged-method | 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

## -- D --

dcastRepeatedMeasures | Convert a longitudinal data.frame to a matrix
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

## -- E --

estimationTime | Estimation time
estimationTime-method | Estimation time
evaluate.lcMethod | Substitute the call arguments for their evaluated values
externalMetric | Compute external model metric(s)
externalMetric-method | Compute external model metric(s)

## -- F --

fit | 'lcMethod' estimation step: logic for fitting the method to the processed data
fit-method | 'lcMethod' estimation step: logic for fitting the method to the processed data
fit-method | lcMetaMethod abstract class
fitted.lcApproxModel | lcApproxModel class
fitted.lcModel | Extract lcModel fitted values
fittedTrajectories | Extract the fitted trajectories
fittedTrajectories-method | Extract the fitted trajectories
formula.lcMethod | Extract formula
formula.lcModel | Extract the formula of a lcModel

## -- G --

generateLongData | Generate longitudinal test data
getArgumentDefaults | Default argument values for the given method specification
getArgumentDefaults-method | Default argument values for the given method specification
getArgumentExclusions | Arguments to be excluded from the specification
getArgumentExclusions-method | Arguments to be excluded from the specification
getCitation | Get citation info
getCitation-method | 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
getLabel-method | Object label
getLcMethod | Get the method specification
getLcMethod-method | Get the method specification
getLcMethod-method | lcMetaMethod abstract class
getName | Object name
getName-method | Object name
getName-method | lcMetaMethod abstract class
getShortName | Object name
getShortName-method | Object name
getShortName-method | lcMetaMethod abstract class

## -- I --

ids | Get the trajectory ids on which the model was fitted
idVariable | Extract the trajectory identifier variable
idVariable-method | Extract the trajectory identifier variable
idVariable-method | lcMetaMethod abstract class
initialize-method | lcMethod initialization
interface-metaMethods | lcMetaMethod abstract class
internalMetric | Compute internal model metric(s)

## -- L --

latrend | Cluster longitudinal data using the specified method
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-parallel | Parallel computation using latrend
latrend-procedure | Longitudinal cluster method ('lcMethod') estimation procedure
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
lcApproxModel | lcApproxModel class
lcApproxModel-class | lcApproxModel class
lcFitConverged | Method fit modifiers
lcFitConverged-class | Method fit modifiers
lcFitMethods | Method fit modifiers
lcFitRep | Method fit modifiers
lcFitRep-class | Method fit modifiers
lcFitRepMax | Method fit modifiers
lcFitRepMin | Method fit modifiers
lcMetaMethod-class | lcMetaMethod abstract class
lcMetaMethods | Method fit modifiers
lcMethod | lcMethod class
lcMethod-class | lcMethod class
lcMethod-estimation | Longitudinal cluster method ('lcMethod') estimation 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
lcMethodFunction | Specify a custom method based on a function
lcMethodFunFEM | Specify a FunFEM method
lcMethodGCKM | Two-step clustering through latent growth curve modeling and k-means
lcMethodKML | Specify a longitudinal k-means (KML) method
lcMethodLcmmGBTM | Specify GBTM method
lcMethodLcmmGMM | Specify GMM method using lcmm
lcMethodLMKM | Two-step clustering through linear regression modeling and k-means
lcMethodMclustLLPA | Longitudinal latent profile analysis
lcMethodMixAK_GLMM | Specify a GLMM iwht a normal mixture in the random effects
lcMethodMixtoolsGMM | Specify mixed mixture regression model using mixtools
lcMethodMixtoolsNPRM | Specify non-parametric estimation for independent repeated measures
lcMethodMixTVEM | Specify a MixTVEM
lcMethodRandom | Specify a random-partitioning method
lcMethods | Generate a list of lcMethod objects
lcMethodStratify | Specify a stratification method
lcModel | Longitudinal cluster result (*'lcModel'*)
lcModel-class | 'lcModel' class
lcModelPartition | Create a lcModel with pre-defined partitioning
lcModels | Construct a list of 'lcModel' objects
lcModels-class | 'lcModels': a list of 'lcModel' objects
lcModelWeightedPartition | Create a lcModel with pre-defined weighted partitioning
length-method | lcMethod argument names
logLik.lcModel | Extract the log-likelihood of a lcModel

## -- M --

max.lcModels | Select the lcModel with the highest metric value
meltRepeatedMeasures | Convert a multiple time series matrix to a data.frame
metric | Compute internal model metric(s)
metric-method | 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

## -- N --

names-method | lcMethod argument names
nClusters | Number of clusters
nClusters-method | Number of clusters
nIds | Number of trajectories
nobs.lcModel | Number of observations used for the lcModel fit

## -- O --

OCC | Odds of correct classification (OCC)

## -- P --

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
plot-lcModel-method | Plot a lcModel
plot-lcModels-method | Grid plot for a list of models
plot-method | Plot a lcModel
plot-method | Grid plot for a list of models
plotClusterTrajectories | Plot cluster trajectories
plotClusterTrajectories-method | Plot cluster trajectories
plotFittedTrajectories | Plot the fitted trajectories
plotFittedTrajectories-method | Plot the fitted trajectories
plotMetric | Plot one or more internal metrics for all lcModels
plotTrajectories | Plot the data trajectories
plotTrajectories-method | Plot the data trajectories
postFit | 'lcMethod' estimation step: logic for post-processing the fitted lcModel
postFit-method | lcMetaMethod abstract class
postFit-method | 'lcMethod' estimation step: logic for post-processing the fitted lcModel
postprob | Posterior probability per fitted trajectory
postprob-method | Posterior probability per fitted trajectory
postprobFromAssignments | Create a posterior probability matrix from a vector of cluster assignments.
predict.lcModel | lcModel predictions
predictAssignments | Predict the cluster assignments for new trajectories
predictAssignments-method | Predict the cluster assignments for new trajectories
predictForCluster | Predict trajectories conditional on cluster membership
predictForCluster-method | lcApproxModel class
predictForCluster-method | Predict trajectories conditional on cluster membership
predictPostprob | Posterior probability for new data
predictPostprob-method | Posterior probability for new data
preFit | 'lcMethod' estimation step: method preparation logic
preFit-method | lcMetaMethod abstract class
preFit-method | 'lcMethod' estimation step: method preparation logic
prepareData | 'lcMethod' estimation step: logic for preparing the training data
prepareData-method | lcMetaMethod abstract class
prepareData-method | 'lcMethod' estimation step: logic for preparing the training data
print.lcMethod | Print the arguments of an lcMethod object
print.lcModels | Print lcModels list concisely

## -- Q --

qqPlot | Quantile-quantile plot

## -- R --

residuals.lcModel | Extract lcModel residuals
responseVariable | Extract response variable
responseVariable-method | lcMetaMethod abstract class
responseVariable-method | Extract response variable

## -- S --

sigma.lcModel | Extract residual standard deviation from a lcModel
strip | Reduce the memory footprint of an object for serialization
strip-method | Reduce the memory footprint of an object for serialization
subset.lcModels | Subsetting a lcModels list based on method arguments
summary.lcModel | Summarize a lcModel

## -- T --

test.latrend | Test the implementation of an lcMethod and associated lcModel subclasses
time.lcModel | Sampling times of a lcModel
timeariable-method | Extract the time variable
timeVariable | Extract the time variable
timeVariable-method | lcMetaMethod abstract class
timeVariable-method | Extract the time variable
trajectories | Get the trajectories
trajectories-method | Get the trajectories
trajectoryAssignments | Get the cluster membership of each trajectory
trajectoryAssignments-method | Get the cluster membership of each trajectory
transformFitted | Helper function for custom lcModel classes implementing fitted.lcModel()
transformFitted-method | Helper function for custom lcModel classes implementing fitted.lcModel()
transformPredict | Helper function for custom lcModel classes implementing predict.lcModel()
transformPredict-method | Helper function for custom lcModel classes implementing predict.lcModel()
tsframe | Convert a multiple time series matrix to a data.frame
tsmatrix | Convert a longitudinal data.frame to a matrix

## -- U --

update.lcMetaMethod | lcMetaMethod abstract class
update.lcMethod | Update a method specification
update.lcModel | Update a lcModel

## -- V --

validate | 'lcMethod' estimation step: method argument validation logic
validate-method | lcMetaMethod abstract class
validate-method | 'lcMethod' estimation step: method argument validation logic

## -- W --

which.weight | Sample an index of a vector weighted by the elements

## -- misc --

$-method | Retrieve and evaluate a lcMethod argument by name
[[-method | Retrieve and evaluate a lcMethod argument by name