A Framework for Clustering Longitudinal Data

Documentation for package ‘latrend’ version 1.6.1

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

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