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latrend (version 1.5.0)

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.

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Install

install.packages('latrend')

Monthly Downloads

354

Version

1.5.0

License

GPL (>= 2)

Issues

Pull Requests

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Repository

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

Maintainer

Niek Den Teuling

Last Published

November 10th, 2022

Functions in latrend (1.5.0)

Check model convergence

latrend-specific assertions

Extract lcModel coefficients

confusionMatrix

Compute the posterior confusion matrix

clusterTrajectories

Extract the cluster trajectories

Number of trajectories per cluster

Update the cluster names

Get the cluster names

deviance.lcModel

lcModel deviance

createTrainDataFolds

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

createTestDataFold

Create the test fold data for validation

.defineInternalDistanceMetrics

Define the distance metrics for multiple types at once

df.residual.lcModel

Extract the residual degrees of freedom from a lcModel

defineInternalMetric

Define an internal metric for lcModels

lcMethod fit process: compose an lcMethod object

clusterProportions

Proportional size of each cluster

defineExternalMetric

Define an external metric for lcModels

Get the model estimation time

Select trajectories

createTestDataFolds

Create all k test folds from the training data

formula.lcMethod

Extract formula

fittedTrajectories

Extract the fitted trajectories for all strata

lcMethod fit process: logic for fitting the method to the processed data

Extract lcModel fitted values

generateLongData

Generate longitudinal test data

formula.lcModel

Extract the formula of a lcModel

Extract the method label.

Get the method specification of a lcModel

Get the (short) name of the lcMethod or Model

getInternalMetricNames

Get the names of the available internal metrics

getArgumentExclusions

Arguments to be excluded for lcMethod subclass

getArgumentDefaults

Default argument values for lcMethod subclass

getExternalMetricDefinition

Get the external metric definition

evaluate.lcMethod

Substitute the call arguments for their evaluated values

externalMetric,lcModel,lcModel-method

Compute external model metric(s)

getInternalMetricDefinition

Get the internal metric definition

getExternalMetricNames

Get the names of the available external metrics

getCall.lcModel

Get the model call

interface-dtwclust

dtwclust interface

clusterTrajectories,lcModelPartition-method

function interface

interface-funFEM

funFEM interface

[[,lcMethod-method

Retrieve and evaluate a lcMethod argument by name

interface-flexmix

flexmix interface

interface-featureBased

featureBased interface

Get the trajectory ids on which the model was fitted

Extract the trajectory identifier variable

Check if object is of Class

interface-crimCV

crimCV interface

interface-akmedoids

akmedoids interface

initialize,lcMethod-method

lcMethod initialization

getArgumentDefaults,lcMethodLcmmGMM-method

interface-mclust

mclust interface

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

latrend-package

latrend: A Framework for Clustering Longitudinal Data

latrend-generics

Method- and model-specific generics defined by the latrend package

interface-metaMethods

lcMetaMethod abstract class

interface-mixAK

mixAK interface

Method fit modifiers

lcMatrixMethod-class

latrend-parallel

Parallel computing using latrend

Cluster longitudinal data

Cluster longitudinal data using bootstrapping

Cluster longitudinal data for a list of method specifications

lcApproxModel-class

lcApproxModel class

interface-mixtvem

mixtvem interface

Cluster longitudinal data repeatedly

Cluster longitudinal data over k folds

Artificial longitudinal dataset comprising three classes

interface-mixtools

mixtools interface

lcMethodFlexmix

Method interface to flexmix()

lcMethodFeature

Feature-based clustering

lcMethodFunction

Specify a custom method based on a function

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

lcMethodAkmedoids

Specify AKMedoids method

Specify a random-partitioning method

lcMethodMixtoolsNPRM

Specify non-parametric estimation for independent repeated measures

lcMethodMclustLLPA

Longitudinal latent profile analysis

lcMethodMixAK_GLMM

Specify a GLMM iwht a normal mixture in the random effects

lcMethodLcmmGMM

Specify GMM method using lcmm

lcMethodFlexmixGBTM

Group-based trajectory modeling using flexmix

Specify a FunFEM method

lcMethodLcmmGBTM

Specify GBTM method

Specify a longitudinal k-means (KML) method

Specify a zero-inflated repeated-measures GBTM method

lcMethodDtwclust

Specify time series clustering via dtwclust

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

Extract the log-likelihood of a lcModel

lcMethodMixtoolsGMM

Specify mixed mixture regression model using mixtools

lcMethodMixTVEM

Specify a MixTVEM

lcMethodStratify

Specify a stratification method

Generate a list of lcMethod objects

lcModel-data-filters

Data filters for lcModel

model.frame.lcModel

Extract model training data

lcModelWeightedPartition

Create a lcModel with pre-defined weighted partitioning

Construct a flat (named) list of lcModel objects

lcModelPartition

Create a lcModel with pre-defined partitioning

Cluster-handling functions for lcModel implementations.

Select the lcModel with the lowest metric value

Compute internal model metric(s)

Select the lcModel with the highest metric value

Mean ignoring NAs

Argument matching with defaults and parent ellipsis expansion

model.data.lcModel

Extract the model data that was used for fitting

Number of observations used for the lcModel fit

Extract the model training data

plot-lcModels-method

Grid plot for a list of models

plot-lcModel-method

Number of trajectories

lcMethod fit process: logic for post-processing the fitted lcModel

names,lcMethod-method

lcMethod argument names

plotTrajectories

Plot the data trajectories

Number of clusters

plotFittedTrajectories

Plot fitted trajectories of a lcModel

plotClusterTrajectories

Plot cluster trajectories

postprobFromAssignments

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

Print lcModels list concisely

lcMethod fit process: logic for preparing the training data

Plot one or more internal metrics for all lcModels

predictForCluster

lcModel prediction conditional on a cluster

Print the arguments of an lcMethod object

Quantile-quantile plot

predictPostprob

lcModel posterior probability prediction

Posterior probability per fitted trajectory

postProbFromObs

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

predict.lcModel

lcModel predictions

predictAssignments

Predict the cluster assignments for new trajectories

responseVariable

Extract the response variable

Test a condition

Extract the time variable

summary.lcModel

Summarize a lcModel

Reduce the lcModel memory footprint for serialization

subset.lcModels

Subsetting a lcModels list based on method arguments

Extract residual standard deviation from a lcModel

lcMethod fit process: method preparation logic

residuals.lcModel

Extract lcModel residuals

Sampling times of a lcModel

Test the implementation of an lcMethod and associated lcModel subclasses

trajectoryAssignments

Get the cluster membership of each trajectory

Extract the trajectories

Convert a longitudinal data.frame to a matrix

Convert a multiple time series matrix to a data.frame

lcMethod fit process: method argument validation logic

weighted.meanNA

Weighted arithmetic mean ignoring NAs

update.lcMethod

Update a method specification

Sample an index of a vector weighted by the elements

transformPredict

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

transformFitted

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

Update a lcModel

Convert a list of lcMethod objects to a lcMethods list

as.data.frame.lcMethods

Convert a list of lcMethod objects to a data.frame

as.list.lcMethod

Extract the method arguments as a list

Convert a list of lcModels to a lcModels list

as.data.frame.lcModels

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

Odds of correct classification (OCC)

Average posterior probability of assignment (APPA)

Biweekly Mean Therapy Adherence of OSA Patients over 1 Year

as.data.frame.lcMethod

Convert lcMethod arguments to a list of atomic types

Biweekly Mean Treatment Adherence of OSA Patients over 1 Year