| latrend: A Framework for Clustering Longitudinal Data | latrend-package |
| Retrieve and evaluate a lcMethod argument by name | $,lcMethod-method [[,lcMethod-method |
| Average posterior probability of assignment (APPA) | APPA |
| Convert lcMethod arguments to a list of atomic types | as.data.frame.lcMethod |
| Convert a list of lcMethod objects to a data.frame | as.data.frame.lcMethods |
| Generate a data.frame containing the argument values per method per row | as.data.frame.lcModels |
| Convert a list of lcMethod objects to a lcMethods list | as.lcMethods |
| Convert a list of lcModels to a lcModels list | as.lcModels |
| Extract the method arguments as a list | as.list.lcMethod |
| Get the cluster names | clusterNames |
| Update the cluster names | clusterNames<- |
| Proportional size of each cluster | clusterProportions clusterProportions,lcModel-method |
| Number of trajectories per cluster | clusterSizes |
| Extract cluster trajectories | clusterTrajectories clusterTrajectories,lcModel-method |
| Extract lcModel coefficients | coef.lcModel |
| 'lcMethod' estimation step: compose an lcMethod object | compose compose,lcMethod-method |
| Compute the posterior confusion matrix | confusionMatrix |
| Check model convergence | converged converged,lcModel-method |
| Create the test fold data for validation | createTestDataFold |
| Create all k test folds from the training data | createTestDataFolds |
| Create the training data for each of the k models in k-fold cross validation evaluation | createTrainDataFolds |
| Define an external metric for lcModels | defineExternalMetric |
| Define an internal metric for lcModels | defineInternalMetric |
| lcModel deviance | deviance.lcModel |
| Extract the residual degrees of freedom from a lcModel | df.residual.lcModel |
| Estimation time | estimationTime estimationTime,lcModel-method estimationTime,lcModels-method estimationTime,list-method |
| Substitute the call arguments for their evaluated values | evaluate.lcMethod |
| Compute external model metric(s) | externalMetric externalMetric,lcModel,lcModel-method externalMetric,lcModels,character-method externalMetric,lcModels,lcModel-method externalMetric,lcModels,lcModels-method externalMetric,lcModels,missing-method externalMetric,list,lcModel-method |
| 'lcMethod' estimation step: logic for fitting the method to the processed data | fit fit,lcMethod-method |
| Extract lcModel fitted values | fitted.lcModel |
| Extract the fitted trajectories | fittedTrajectories fittedTrajectories,lcModel-method |
| Extract formula | formula.lcMethod |
| Extract the formula of a lcModel | formula.lcModel |
| Generate longitudinal test data | generateLongData |
| Default argument values for the given method specification | getArgumentDefaults getArgumentDefaults,lcMethod-method |
| Arguments to be excluded from the specification | getArgumentExclusions getArgumentExclusions,lcMethod-method |
| Get citation info | getCitation getCitation,lcMethod-method getCitation,lcModel-method |
| Get the external metric definition | getExternalMetricDefinition |
| Get the names of the available external metrics | getExternalMetricNames |
| Get the internal metric definition | getInternalMetricDefinition |
| Get the names of the available internal metrics | getInternalMetricNames |
| Object label | getLabel getLabel,lcMethod-method getLabel,lcModel-method |
| Get the method specification | getLcMethod getLcMethod,lcModel-method |
| Object name | getName getName,lcMethod-method getName,lcModel-method getName,NULL-method getShortName getShortName,lcMethod-method getShortName,lcModel-method getShortName,NULL-method |
| Get the trajectory ids on which the model was fitted | ids |
| Extract the trajectory identifier variable | idVariable idVariable,ANY-method idVariable,lcMethod-method idVariable,lcModel-method |
| lcMethod initialization | initialize,lcMethod-method |
| lcMetaMethod abstract class | compose,lcMetaMethod-method fit,lcFitConverged-method fit,lcFitRep-method fit,lcMetaMethod-method getLcMethod,lcMetaMethod-method getName,lcMetaMethod-method getShortName,lcMetaMethod-method idVariable,lcMetaMethod-method interface-metaMethods lcMetaMethod-class postFit,lcMetaMethod-method preFit,lcMetaMethod-method prepareData,lcMetaMethod-method responseVariable,lcMetaMethod-method timeVariable,lcMetaMethod-method update.lcMetaMethod validate,lcFitConverged-method validate,lcFitRep-method validate,lcMetaMethod-method |
| Cluster longitudinal data using the specified method | latrend |
| High-level approaches to longitudinal clustering | latrend-approaches |
| Longitudinal dataset representation | latrend-data |
| Overview of *'lcMethod'* estimation functions | latrend-estimation |
| Generics used by latrend for different classes | latrend-generics |
| Supported methods for longitudinal clustering | latrend-methods |
| Metrics | latrend-metrics |
| Parallel computation using latrend | latrend-parallel |
| Cluster longitudinal data for a list of method specifications | latrendBatch |
| Cluster longitudinal data using bootstrapping | latrendBoot |
| Cluster longitudinal data over k folds | latrendCV |
| Artificial longitudinal dataset comprising three classes | latrendData |
| Cluster longitudinal data repeatedly | latrendRep |
| lcApproxModel class | fitted.lcApproxModel lcApproxModel lcApproxModel-class predictForCluster,lcApproxModel-method |
| Method fit modifiers | lcFitConverged lcFitConverged-class lcFitMethods lcFitRep lcFitRep-class lcFitRepMax lcFitRepMin lcMetaMethods |
| lcMethod class | lcMethod lcMethod-class |
| Longitudinal cluster method ('lcMethod') estimation procedure | latrend-procedure lcMethod-estimation lcMethod-steps |
| Specify AKMedoids method | lcMethodAkmedoids |
| Specify a zero-inflated repeated-measures GBTM method | lcMethodCrimCV |
| Specify time series clustering via dtwclust | lcMethodDtwclust |
| Feature-based clustering | lcMethodFeature |
| Method interface to flexmix() | lcMethodFlexmix |
| Group-based trajectory modeling using flexmix | lcMethodFlexmixGBTM |
| Specify a custom method based on a function | lcMethodFunction |
| Specify a FunFEM method | lcMethodFunFEM |
| Two-step clustering through latent growth curve modeling and k-means | lcMethodGCKM |
| Specify a longitudinal k-means (KML) method | lcMethodKML |
| Specify GBTM method | lcMethodLcmmGBTM |
| Specify GMM method using lcmm | lcMethodLcmmGMM |
| Two-step clustering through linear regression modeling and k-means | lcMethodLMKM |
| Longitudinal latent profile analysis | lcMethodMclustLLPA |
| Specify a GLMM iwht a normal mixture in the random effects | lcMethodMixAK_GLMM |
| Specify mixed mixture regression model using mixtools | lcMethodMixtoolsGMM |
| Specify non-parametric estimation for independent repeated measures | lcMethodMixtoolsNPRM |
| Specify a MixTVEM | lcMethodMixTVEM |
| Specify a random-partitioning method | lcMethodRandom |
| Generate a list of lcMethod objects | lcMethods |
| Specify a stratification method | lcMethodStratify |
| Longitudinal cluster result (*'lcModel'*) | lcModel |
| 'lcModel' class | lcModel-class |
| Create a lcModel with pre-defined partitioning | lcModelPartition |
| Construct a list of 'lcModel' objects | lcModels |
| 'lcModels': a list of 'lcModel' objects | lcModels-class |
| Create a lcModel with pre-defined weighted partitioning | lcModelWeightedPartition |
| Extract the log-likelihood of a lcModel | logLik.lcModel |
| Select the lcModel with the highest metric value | max.lcModels |
| Compute internal model metric(s) | internalMetric metric metric,lcModel-method metric,lcModels-method metric,list-method |
| Select the lcModel with the lowest metric value | min.lcModels |
| Extract the model data that was used for fitting | model.data.lcModel |
| Extract model training data | model.frame.lcModel |
| lcMethod argument names | length,lcMethod-method names,lcMethod-method |
| Number of clusters | nClusters nClusters,lcModel-method |
| Number of trajectories | nIds |
| Number of observations used for the lcModel fit | nobs.lcModel |
| Odds of correct classification (OCC) | OCC |
| Weekly Mean PAP Therapy Usage of OSA Patients in the First 3 Months | PAP.adh |
| Biweekly Mean PAP Therapy Adherence of OSA Patients over 1 Year | PAP.adh1y |
| Plot a lcModel | plot,lcModel,ANY-method plot,lcModel-method plot-lcModel-method |
| Grid plot for a list of models | plot,lcModels,ANY-method plot,lcModels-method plot-lcModels-method |
| Plot cluster trajectories | plotClusterTrajectories plotClusterTrajectories,data.frame-method plotClusterTrajectories,lcModel-method |
| Plot the fitted trajectories | plotFittedTrajectories plotFittedTrajectories,lcModel-method |
| Plot one or more internal metrics for all lcModels | plotMetric |
| Plot the data trajectories | plotTrajectories plotTrajectories,ANY-method plotTrajectories,data.frame-method plotTrajectories,lcModel-method |
| 'lcMethod' estimation step: logic for post-processing the fitted lcModel | postFit postFit,lcMethod-method |
| Posterior probability per fitted trajectory | postprob postprob,lcModel-method |
| Create a posterior probability matrix from a vector of cluster assignments. | postprobFromAssignments |
| lcModel predictions | predict.lcModel |
| Predict the cluster assignments for new trajectories | predictAssignments predictAssignments,lcModel-method |
| Predict trajectories conditional on cluster membership | predictForCluster predictForCluster,lcModel-method |
| Posterior probability for new data | predictPostprob predictPostprob,lcModel-method |
| 'lcMethod' estimation step: method preparation logic | preFit preFit,lcMethod-method |
| 'lcMethod' estimation step: logic for preparing the training data | prepareData prepareData,lcMethod-method |
| Print the arguments of an lcMethod object | print.lcMethod |
| Print lcModels list concisely | print.lcModels |
| Quantile-quantile plot | qqPlot |
| Extract lcModel residuals | residuals.lcModel |
| Extract response variable | responseVariable responseVariable,lcMethod-method responseVariable,lcModel-method |
| Extract residual standard deviation from a lcModel | sigma.lcModel |
| Reduce the memory footprint of an object for serialization | strip strip,ANY-method strip,lcMethod-method strip,lcModel-method |
| Subsetting a lcModels list based on method arguments | subset.lcModels |
| Summarize a lcModel | summary.lcModel |
| Test the implementation of an lcMethod and associated lcModel subclasses | test.latrend |
| Sampling times of a lcModel | time.lcModel |
| Extract the time variable | timeariable,ANY-method timeVariable timeVariable,ANY-method timeVariable,lcMethod-method timeVariable,lcModel-method |
| Get the trajectories | trajectories trajectories,call-method trajectories,data.frame-method trajectories,lcModel-method trajectories,matrix-method |
| Get the cluster membership of each trajectory | trajectoryAssignments trajectoryAssignments,lcModel-method trajectoryAssignments,matrix-method |
| Helper function for custom lcModel classes implementing fitted.lcModel() | transformFitted transformFitted,data.frame,lcModel-method transformFitted,list,lcModel-method transformFitted,matrix,lcModel-method transformFitted,NULL,lcModel-method |
| Helper function for custom lcModel classes implementing predict.lcModel() | transformPredict transformPredict,data.frame,lcModel-method transformPredict,matrix,lcModel-method transformPredict,NULL,lcModel-method transformPredict,vector,lcModel-method |
| Convert a multiple time series matrix to a data.frame | meltRepeatedMeasures tsframe |
| Convert a longitudinal data.frame to a matrix | dcastRepeatedMeasures tsmatrix |
| Update a method specification | update.lcMethod |
| Update a lcModel | update.lcModel |
| 'lcMethod' estimation step: method argument validation logic | validate validate,lcMethod-method |
| Sample an index of a vector weighted by the elements | which.weight |
