latrend1.6.2 package

Average posterior probability of assignment (APPA)

Convert lcMethod arguments to a list of atomic types

Convert a list of lcMethod objects to a data.frame

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

Convert a list of lcMethod objects to a lcMethods list

Convert a list of lcModels to a lcModels list

Extract the method arguments as a list

latrend-specific assertions

Update the cluster names

Get the cluster names

Proportional size of each cluster

Number of trajectories per cluster

Extract cluster trajectories

Extract lcModel coefficients

lcMethod estimation step: compose an lcMethod object

Compute the posterior confusion matrix

Check model convergence

Create the test fold data for validation

Create all k test folds from the training data

Create the training data for each of the k models in k-fold cross vali...

Define an external metric for lcModels

Define an internal metric for lcModels

lcModel deviance

Extract the residual degrees of freedom from a lcModel

Define the distance metrics for multiple types at once

Guess the response variable

Select trajectories

Estimation time

Substitute the call arguments for their evaluated values

Compute external model metric(s)

lcMethod estimation step: logic for fitting the method to the proces...

Extract lcModel fitted values

Extract the fitted trajectories

Extract formula

Extract the formula of a lcModel

Generate longitudinal test data

Default argument values for the given method specification

Arguments to be excluded from the specification

Get the model call

Get citation info

Get the external metric definition

Get the names of the available external metrics

Get the internal metric definition

Get the names of the available internal metrics

Object label

Get the method specification

Object name

Get the trajectory ids on which the model was fitted

Extract the trajectory identifier variable

Retrieve and evaluate a lcMethod argument by name

lcMethod initialization

akmedoids interface

crimCV interface

function interface

dtwclust interface

featureBased interface

flexmix interface

funFEM interface

kml interface

lcmm interface

mclust interface

lcMetaMethod abstract class

mixAK interface

mixtools interface

mixtvem interface

Check if object is of Class

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

High-level approaches to longitudinal clustering

Longitudinal dataset representation

Overview of ‘lcMethod’ estimation functions

Generics used by latrend for different classes

Supported methods for longitudinal clustering

Metrics

latrend: A Framework for Clustering Longitudinal Data

Parallel computation using latrend

Cluster longitudinal data using the specified method

Cluster longitudinal data for a list of method specifications

Cluster longitudinal data using bootstrapping

Cluster longitudinal data over k folds

Cluster longitudinal data repeatedly

lcApproxModel class

Method fit modifiers

lcMatrixMethod

lcMethod class

Longitudinal cluster method (lcMethod) estimation procedure

Specify AKMedoids method

Specify a zero-inflated repeated-measures GBTM method

Specify time series clustering via dtwclust

Feature-based clustering

Method interface to flexmix()

Group-based trajectory modeling using flexmix

Specify a custom method based on a function

Specify a FunFEM method

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

Specify a longitudinal k-means (KML) method

Specify GBTM method

Specify GMM method using lcmm

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

Longitudinal latent profile analysis

Specify a GLMM iwht a normal mixture in the random effects

Specify mixed mixture regression model using mixtools

Specify non-parametric estimation for independent repeated measures

Specify a MixTVEM

Specify a random-partitioning method

Generate a list of lcMethod objects

Specify a stratification method

lcModel class

Data filters for lcModel

Cluster-handling functions for lcModel implementations.

Longitudinal cluster result (‘lcModel’ )

Create a lcModel with pre-defined partitioning

lcModels: a list of lcModel objects

Construct a list of lcModel objects

Create a lcModel with pre-defined weighted partitioning

Extract the log-likelihood of a lcModel

Argument matching with defaults and parent ellipsis expansion

Select the lcModel with the highest metric value

Mean ignoring NAs

Compute internal model metric(s)

Select the lcModel with the lowest metric value

Extract the model data that was used for fitting

Extract the model training data

Extract model training data

lcMethod argument names

Number of clusters

Number of trajectories

Number of observations used for the lcModel fit

Odds of correct classification (OCC)

Plot a lcModel

Grid plot for a list of models

Plot cluster trajectories

Plot the fitted trajectories

Plot one or more internal metrics for all lcModels

Plot the data trajectories

lcMethod estimation step: logic for post-processing the fitted lcMod...

Posterior probability per fitted trajectory

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

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

lcModel predictions

Predict the cluster assignments for new trajectories

Predict trajectories conditional on cluster membership

Posterior probability for new data

lcMethod estimation step: method preparation logic

lcMethod estimation step: logic for preparing the training data

Print the arguments of an lcMethod object

Print lcModels list concisely

Quantile-quantile plot

Extract lcModel residuals

Extract response variable

Extract residual standard deviation from a lcModel

Reduce the memory footprint of an object for serialization

Subsetting a lcModels list based on method arguments

Summarize a lcModel

Test the implementation of an lcMethod and associated lcModel subclass...

Test a condition

Sampling times of a lcModel

Extract the time variable

Get the trajectories

Get the cluster membership of each trajectory

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

Helper function for custom lcModel classes implementing predict.lcMode...

Convert a multiple time series matrix to a data.frame

Convert a longitudinal data.frame to a matrix

Update a method specification

Update a lcModel

lcMethod estimation step: method argument validation logic

Weighted arithmetic mean ignoring NAs

Sample an index of a vector weighted by the elements