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