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latrend (version 1.6.0)

A Framework for Clustering Longitudinal Data

Description

A framework for clustering longitudinal datasets in a standardized way. The package provides an interface to existing R packages for clustering longitudinal univariate trajectories, facilitating reproducible and transparent analyses. Additionally, standard tools are provided to support cluster analyses, including repeated estimation, model validation, and model assessment. The interface enables users to compare results between methods, and to implement and evaluate new methods with ease. The 'akmedoids' package is available from .

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Version

Install

install.packages('latrend')

Monthly Downloads

398

Version

1.6.0

License

GPL (>= 2)

Issues

Pull Requests

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Repository

https://github.com/philips-software/latrend

Homepage

https://philips-software.github.io/latrend/

Maintainer

Niek Den Teuling

Last Published

February 18th, 2024

Functions in latrend (1.6.0)

Compute external model metric(s)

getCall.lcModel

Get the model call

getArgumentExclusions

Arguments to be excluded from the specification

interface-dtwclust

dtwclust interface

defineInternalMetric

Define an internal metric for lcModels

Construct a list of lcModel objects

evaluate.lcMethod

Substitute the call arguments for their evaluated values

Average posterior probability of assignment (APPA)

Odds of correct classification (OCC)

formula.lcModel

Extract the formula of a lcModel

Test a condition

latrend-estimation

Overview of lcMethod estimation functions

createTestDataFolds

Create all k test folds from the training data

interface-mixAK

mixAK interface

lcMethod estimation step: method preparation logic

Two-step clustering through linear regression modeling and k-means

Extract the log-likelihood of a lcModel

lcMethodLcmmGBTM

Specify GBTM method

clusterTrajectories

Extract cluster trajectories

Cluster longitudinal data for a list of method specifications

Get citation info

interface-mixtools

mixtools interface

lcMethodMixAK_GLMM

Specify a GLMM iwht a normal mixture in the random effects

Longitudinal dataset representation

Number of clusters

interface-crimCV

crimCV interface

lcMethod estimation step: compose an lcMethod object

Update a lcModel

Extract lcModel coefficients

lcMethod estimation step: method argument validation logic

Number of trajectories

defineExternalMetric

Define an external metric for lcModels

Sampling times of a lcModel

confusionMatrix

Compute the posterior confusion matrix

Longitudinal cluster result (lcModel)

interface-featureBased

featureBased interface

getInternalMetricDefinition

Get the internal metric definition

Convert a list of lcMethod objects to a lcMethods list

as.data.frame.lcModels

Generate a data.frame containing the argument values per method per row

Number of trajectories per cluster

generateLongData

Generate longitudinal test data

Convert a list of lcModels to a lcModels list

interface-mclust

mclust interface

interface-mixtvem

mixtvem interface

Generate a list of lcMethod objects

as.list.lcMethod

Extract the method arguments as a list

interface-funFEM

funFEM interface

Biweekly Mean PAP Therapy Adherence of OSA Patients over 1 Year

[[,lcMethod-method

Retrieve and evaluate a lcMethod argument by name

getInternalMetricNames

Get the names of the available internal metrics

Check if object is of Class

latrend-specific assertions

Cluster longitudinal data repeatedly

as.data.frame.lcMethod

Convert lcMethod arguments to a list of atomic types

Check whether the argument of a lcMethod has a defined value.

Weekly Mean PAP Therapy Usage of OSA Patients in the First 3 Months

getExternalMetricNames

Get the names of the available external metrics

Convert a longitudinal data.frame to a matrix

latrend-generics

Generics used by latrend for different classes

latrend-methods

Supported methods for longitudinal clustering

Specify a longitudinal k-means (KML) method

df.residual.lcModel

Extract the residual degrees of freedom from a lcModel

Two-step clustering through latent growth curve modeling and k-means

lcMethod-estimation

Longitudinal cluster method (lcMethod) estimation procedure

model.frame.lcModel

Extract model training data

as.data.frame.lcMethods

Convert a list of lcMethod objects to a data.frame

Check model convergence

model.data.lcModel

Extract the model data that was used for fitting

Get the trajectory ids on which the model was fitted

formula.lcMethod

Extract formula

createTrainDataFolds

Create the training data for each of the k models in k-fold cross validation evaluation

lcMethodAkmedoids

Specify AKMedoids method

lcApproxModel-class

lcApproxModel class

getArgumentDefaults

Default argument values for the given method specification

interface-flexmix

flexmix interface

lcModelPartition

Create a lcModel with pre-defined partitioning

deviance.lcModel

lcModel deviance

lcModelWeightedPartition

Create a lcModel with pre-defined weighted partitioning

getExternalMetricDefinition

Get the external metric definition

plotClusterTrajectories

Plot cluster trajectories

lcMethodMixtoolsGMM

Specify mixed mixture regression model using mixtools

plotTrajectories

Plot the data trajectories

Get the method specification

latrend-approaches

High-level approaches to longitudinal clustering

interface-metaMethods

lcMetaMethod abstract class

lcMethodMixtoolsNPRM

Specify non-parametric estimation for independent repeated measures

lcMethodDtwclust

Specify time series clustering via dtwclust

Cluster longitudinal data using bootstrapping

lcMethodMixTVEM

Specify a MixTVEM

Specify a zero-inflated repeated-measures GBTM method

lcMethodFeature

Feature-based clustering

Compute internal model metric(s)

Cluster longitudinal data over k folds

Plot one or more internal metrics for all lcModels

Posterior probability per fitted trajectory

Extract the time variable

Reduce the memory footprint of an object for serialization

lcModels: a list of lcModel objects

Mean ignoring NAs

predictPostprob

Posterior probability for new data

plotFittedTrajectories

Plot the fitted trajectories

Sample an index of a vector weighted by the elements

predict.lcModel

lcModel predictions

plot-lcModels-method

Grid plot for a list of models

responseVariable

Extract response variable

lcMethod estimation step: logic for preparing the training data

Extract the trajectories

lcModel class

Argument matching with defaults and parent ellipsis expansion

latrend-metrics

Select trajectories

Artificial longitudinal dataset comprising three classes

initialize,lcMethod-method

lcMethod initialization

subset.lcModels

Subsetting a lcModels list based on method arguments

weighted.meanNA

Weighted arithmetic mean ignoring NAs

Update the cluster names

latrend-package

latrend: A Framework for Clustering Longitudinal Data

Extract lcModel fitted values

Estimation time

Specify a random-partitioning method

lcMethod estimation step: logic for fitting the method to the processed data

Extract the model training data

Method fit modifiers

Select the lcModel with the highest metric value

fittedTrajectories

Extract the fitted trajectories

Specify a FunFEM method

names,lcMethod-method

lcMethod argument names

Quantile-quantile plot

postprobFromAssignments

Create a posterior probability matrix from a vector of cluster assignments.

clusterTrajectories,lcModelPartition-method

function interface

Get the cluster names

getArgumentDefaults,lcMethodLcmmGMM-method

interface-akmedoids

akmedoids interface

residuals.lcModel

Extract lcModel residuals

lcMatrixMethod-class

lcMethodFunction

Specify a custom method based on a function

clusterProportions

Proportional size of each cluster

Extract residual standard deviation from a lcModel

update.lcMethod

Update a method specification

.defineInternalDistanceMetrics

Define the distance metrics for multiple types at once

latrend-parallel

Parallel computation using latrend

Extract the trajectory identifier variable

predictAssignments

Predict the cluster assignments for new trajectories

lcMethodFlexmix

Method interface to flexmix()

lcModel-data-filters

Data filters for lcModel

Cluster longitudinal data using the specified method

lcMethodStratify

Specify a stratification method

lcMethodLcmmGMM

Specify GMM method using lcmm

summary.lcModel

Summarize a lcModel

Cluster-handling functions for lcModel implementations.

Select the lcModel with the lowest metric value

plot-lcModel-method

Print the arguments of an lcMethod object

trajectoryAssignments

Get the cluster membership of each trajectory

predictForCluster

Predict trajectories conditional on cluster membership

Convert a multiple time series matrix to a data.frame

transformFitted

Helper function for custom lcModel classes implementing fitted.lcModel()

lcMethodFlexmixGBTM

Group-based trajectory modeling using flexmix

Print lcModels list concisely

lcMethodMclustLLPA

Longitudinal latent profile analysis

lcMethod estimation step: logic for post-processing the fitted lcModel

Number of observations used for the lcModel fit

postProbFromObs

Compute the id-specific postprob matrix from a given observation-level postprob matrix

transformPredict

Helper function for custom lcModel classes implementing predict.lcModel()

createTestDataFold

Create the test fold data for validation

Test the implementation of an lcMethod and associated lcModel subclasses