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latrend (version 1.2.1)

A Framework for Clustering Longitudinal Data

Description

A framework for clustering longitudinal datasets in a standardized way. 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

421

Version

1.2.1

License

GPL (>= 2)

Issues

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Repository

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

Maintainer

Niek Den Teuling

Last Published

January 6th, 2022

Functions in latrend (1.2.1)

Biweekly Mean Treatment Adherence of OSA Patients over 1 Year

Convert a list of lcMethod objects to a lcMethods list

as.data.frame.lcMethods

Convert a list of lcMethod objects to a data.frame

Average posterior probability of assignment (APPA)

as.data.frame.lcModels

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

as.list.lcMethod

Extract the method arguments as a list

as.data.frame.lcMethod

Convert lcMethod arguments to a list of atomic types

Odds of correct classification (OCC)

Convert a list of lcModels to a lcModels list

latrend-specific assertions

Check model convergence

clusterTrajectories

Extract the cluster trajectories

createTrainDataFolds

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

createTestDataFolds

Create all k test folds from the training data

lcMethod fit process: compose an lcMethod object

confusionMatrix

Compute the posterior confusion matrix

defineInternalMetric

Define an internal metric for lcModels

Extract lcModel coefficients

deviance.lcModel

lcModel deviance

Get the model estimation time

evaluate.lcMethod

Substitute the call arguments for their evaluated values

createTestDataFold

Create the test fold data for validation

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

externalMetric,lcModel,lcModel-method

Compute external model metric(s)

Update the cluster names

Get the cluster names

getExternalMetricNames

Get the names of the available external metrics

generateLongData

Generate longitudinal test data

getArgumentDefaults

Default argument values for lcMethod subclass

getExternalMetricDefinition

Get the external metric definition

defineExternalMetric

Define an external metric for lcModels

dcastRepeatedMeasures

Cast a longitudinal data.frame to a matrix

Extract the trajectory identifier variable

Get the (short) name of the lcMethod or Model

Extract lcModel fitted values

interface-crimCV

crimCV interface

interface-mixtvem

mixtvem interface

interface-flexmix

flexmix interface

interface-funFEM

funFEM interface

interface-custom

custom interface

Check if object is of Class

formula.lcMethod

Extract formula

Get the trajectory ids on which the model was fitted

formula.lcModel

Extract the formula of a lcModel

[[,lcMethod-method

Retrieve and evaluate a lcMethod argument by name

interface-dtwclust

dtwclust interface

clusterProportions

Proportional size of each cluster

latrend-generics

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

Specify a FunFEM method

lcApproxModel-class

lcApproxModel class

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

Extract the method label.

df.residual.lcModel

Extract the residual degrees of freedom from a lcModel

getArgumentExclusions

Arguments to be excluded for lcMethod subclass

Number of trajectories per cluster

.defineInternalDistanceMetrics

Define the distance metrics for multiple types at once

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

getCall.lcModel

Get the model call

Cluster longitudinal data repeatedly

lcMethodMixAK_GLMM

Specify a GLMM iwht a normal mixture in the random effects

Artificial longitudinal dataset comprising three classes

lcMethodMixTVEM

Specify a MixTVEM

lcMethodFlexmix

Method interface to flexmix()

lcMatrixMethod-class

latrend-parallel

Parallel computing using latrend

lcMethodAkmedoids

Specify AKMedoids method

interface-akmedoids

akmedoids interface

Get the method specification of a lcModel

initialize,lcMethod-method

lcMethod initialization

Specify a zero-inflated repeated-measures GBTM method

latrend-package

latrend: A Framework for Clustering Longitudinal Data

lcMethodFlexmixGBTM

Group-based trajectory modeling using flexmix

fittedTrajectories

Extract the fitted trajectories for all strata

Generate a list of lcMethod objects

Select the lcModel with the highest metric value

Mean ignoring NAs

plotClusterTrajectories

Plot cluster trajectories

Specify a custom method based on a model function

lcModel-data-filters

Data filters for lcModel

Cluster-handling functions for lcModel implementations.

plot-lcModels-method

Grid plot for a list of models

plot-lcModel-method

Specify a random-partitioning method

interface-featureBased

featureBased interface

lcMethodStratify

Specify a stratification method

getInternalMetricDefinition

Get the internal metric definition

lcMethodMixtoolsGMM

Specify mixed mixture regression model using mixtools

Extract the log-likelihood of a lcModel

lcMethodMixtoolsNPRM

Specify non-parametric estimation for independent repeated measures

Argument matching with defaults and parent ellipsis expansion

model.data.lcModel

Extract the model data that was used for fitting

Cluster longitudinal data for a list of method specifications

interface-mixAK

mixAK interface

interface-mixtools

mixtools interface

Cluster longitudinal data

lcMethod fit process: method preparation logic

lcMethodLcmmGBTM

Specify GBTM method

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

Specify a longitudinal k-means (KML) method

lcMethodLcmmGMM

Specify GMM method using lcmm

predict.lcModel

lcModel predictions

plotFittedTrajectories

Plot fitted trajectories of a lcModel

Posterior probability per fitted trajectory

getInternalMetricNames

Get the names of the available internal metrics

getArgumentDefaults,lcMethodLcmmGMM-method

Reduce the lcModel memory footprint for serialization

Construct a flat (named) list of lcModel objects

lcModelWeightedPartition

Create a lcModel with pre-defined weighted partitioning

meltRepeatedMeasures

Convert a repeated measures data matrix to a data.frame

subset.lcModels

Subsetting a lcModels list based on method arguments

Extract the time variable

postprobFromAssignments

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

Extract the trajectories

interface-mclust

mclust interface

Cluster longitudinal data using bootstrapping

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

Compute internal model metric(s)

interface-longclust

longclust interface

predictAssignments

Predict the cluster assignments for new trajectories

predictForCluster

lcModel prediction conditional on a cluster

postProbFromObs

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

Cluster longitudinal data over k folds

Select the lcModel with the lowest metric value

Plot one or more internal metrics for all lcModels

Extract the model training data

lcMethodDtwclust

Specify time series clustering via dtwclust

update.lcMethod

Update a method specification

transformPredict

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

model.frame.lcModel

Extract model training data

Quantile-quantile plot

plotTrajectories

Plot the data trajectories

lcMethodFeature

Feature-based clustering

lcMethodLongclust

Specify Longclust method

lcMethodMclustLLPA

Longitudinal latent profile analysis

trajectoryAssignments

Get the cluster membership of each trajectory

transformFitted

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

lcModelPartition

Create a lcModel with pre-defined partitioning

Number of clusters

Specify a model based on a pre-computed result.

Extract the number of observations from a lcModel

names,lcMethod-method

lcMethod argument names

Number of trajectories

Print the arguments of an lcMethod object

Print lcModels list concisely

predictPostprob

lcModel posterior probability prediction

lcMethod fit process: logic for preparing the training data

Sampling times of a lcModel

Update a lcModel

summary.lcModel

Summarize a lcModel

lcMethod fit process: method argument validation logic

Sample an index of a vector weighted by the elements

Extract residual standard deviation from a lcModel

weighted.meanNA

Weighted arithmetic mean ignoring NAs

responseVariable

Extract the response variable

residuals.lcModel

Extract lcModel residuals