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latrend (version 1.0.1)

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

1.0.1

License

GPL (>= 2)

Issues

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Repository

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

Maintainer

Niek Den Teuling

Last Published

November 18th, 2020

Functions in latrend (1.0.1)

$,lcMethod-method

Retrieve and evaluate a lcMethod argument by name

Convert a list of lcModels to a lcModels list

Convert a list of lcMethod objects to a lcMethods list

as.data.frame.lcMethod

Convert lcMethod arguments to a list of atomic types

as.data.frame.lcModels

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

Update the cluster names

as.data.frame.lcMethods

Convert a list of lcMethod objects to a data.frame

as.list.lcMethod

Extract the method arguments as a list

latrend-specific assertions

confusionMatrix

Compute the posterior confusion matrix

Get the cluster names

Number of strata per cluster

clusterTrajectories

Extract the cluster trajectories

Get the model estimation time

clusterProportions

Proportional size of each cluster

evaluate.lcMethod

Substitute the call arguments for their evaluated values

defineExternalMetric

Define an external metric for lcModels

createTestDataFolds

Create all k test folds from the training data

Check model convergence

getInternalMetricDefinition

Get the internal metric definition

createTestDataFold

Create the test fold data for validation

Get the method specification of a lcModel

defineInternalMetric

Define an internal metric for lcModels

externalMetric,lcModel,lcModel-method

Compute external model metric(s)

Extract lcModel fitted values

Extract the trajectory identifier variable

Coefficients of a lcModel

Create a lcMethod object from a call

lcMethodLongclust

Specify Longclust method

lcMethodAKMedoids

Specify AKMedoids method

Specify a zero-inflated repeated-measures GBTM method

getInternalMetricNames

Get the names of the available internal metrics

interface-featureBased

featureBased interface

deviance.lcModel

lcModel deviance

interface-mclust

mclust interface

interface-crimCV

crimCV interface

interface-akmedoids

akmedoids interface

generateLongData

Generate longitudinal test data

lcMethodFlexmix

Method interface to flexmix()

getCall.lcModel

Get the model call

getExternalMetricDefinition

Get the external metric definition

interface-mixAK

mixAK interface

df.residual.lcModel

Extract the residual degrees of freedom from a lcModel

Specify a custom method based on a model function

interface-dtwclust

dtwclust interface

getExternalMetricNames

Get the names of the available external metrics

interface-custom

custom interface

latrend-generics

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

createTrainDataFolds

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

dcastRepeatedMeasures

Cast a longitudinal data.frame to a matrix

Print lcModels list concisely

formula.lcMethod

Extract formula

formula.lcModel

Extract the formula of a lcModel

Get the unique ids included in this model

Cluster longitudinal data using bootstrapping

interface-funFEM

funFEM interface

[[,lcMethod-method

Retrieve and evaluate a lcMethod argument by name

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

Check if object is of Class

latrend-package

latrend: Framework for Clustering Longitudinal Data

lcApproxModel-class

lcApproxModel class

interface-flexmix

flexmix interface

lcMethodLcmmGMM

Specify GMM method using lcmm

Cluster longitudinal data over k folds

lcMatrixMethod-class

lcMethodFlexmixGBTM

Group-based trajectory modeling using flexmix

Number of strata

Specify a FunFEM method

Number of clusters

getName,lcMethodLcmmGMM-method

Cluster-handling functions for lcModel implementations.

lcMethodStratify

Specify a stratification method

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

interface-longclust

longclust interface

plot,lcModel,ANY-method

lcModel-data-filters

Data filters for lcModel

interface-mixtools

mixtools interface

lcMethodDtwclust

Specify time series clustering via dtwclust

Specify a model based on a pre-computed result.

lcMethodMixtoolsGMM

Specify mixed mixture regression model using mixtools

Cluster longitudinal data

lcMethodLcmmGBTM

Specify GBTM method

lcModelWeightedPartition

Create a lcModel with pre-defined weighted partitioning

lcMethodMclustLLPA

Longitudinal latent profile analysis

lcMethodMixAK_GLMM

Specify a GLMM iwht a normal mixture in the random effects

Extract the log-likelihood of a lcModel

lcMethodTwoStep

Two-step clustering

Argument matching with defaults and parent ellipsis expansion

Cluster longitudinal data for a list of model specifications

Plot one or more internal metrics for all lcModels

Compute internal model metric(s)

Select the lcModel with the lowest metric value

postprobFromAssignments

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

predict.lcModel

lcModel predictions

summary.lcModel

Summarize a lcModel

Extract the number of observations from a lcModel

transformFitted

Helper function for ensuring the right fitted() output

lcMethodMixTVEM

Specify a MixTVEM

model.data.lcModel

Extract the model data that was used for fitting

Create a lcMethod object of the specified type and arguments

interface-mixtvem

mixtvem interface

Synthetic longitudinal dataset comprising three classes

model.frame.lcModel

Extract model training data

transformLatrendData

Transform latrend input data into the right format

postProbFromObs

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

Construct a flat (named) list of lcModel objects

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

Cluster longitudinal data repeatedly

lcMethodMixtoolsNPRM

Specify non-parametric estimation for independent repeated measures

Generate a list of lcMethod objects

Specify a random-partitioning method

Quantile-quantile plot

plotClusterTrajectories

Plot cluster trajectories

Specify a longitudinal k-means (KML) method

predictAssignments

Predict the cluster assignments for new trajectories

Strip a lcModel for serialization

predictForCluster

lcModel prediction for a specific cluster

Extract residual standard deviation from a lcModel

transformPredict

Helper function that matches the output to the specified newdata

Select the lcModel with the highest metric value

update.lcMethod

Update a method specification

Update a lcModel

meltRepeatedMeasures

Convert a repeated measures data matrix to a data.frame

Posterior probability per fitted id

lcModelPartition

Create a lcModel with pre-defined partitioning

residuals.lcModel

Extract lcModel residuals

subset.lcModels

Subsetting a lcModels list based on method arguments

responseVariable

Extract the response variable

Extract the time variable

Extract the fitted trajectories for all strata

weighted.meanNA

Weighted arithmetic mean ignoring NAs

trajectoryAssignments

Get the cluster membership of each trajectory

plotTrajectories

Plot trajectories

Extract the model training data

Print the arguments of an lcMethod object

predictPostprob

lcModel posterior probability prediction

Sample an index of a vector weighted by the elements

Sampling times of a lcModel