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lcmm (version 1.8.1)

Extended Mixed Models Using Latent Classes and Latent Processes

Description

Estimation of various extensions of the mixed models including latent class mixed models, joint latent latent class mixed models and mixed models for curvilinear univariate or multivariate longitudinal outcomes using a maximum likelihood estimation method.

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Version

Install

install.packages('lcmm')

Monthly Downloads

6,659

Version

1.8.1

License

GPL (>= 2.0)

Maintainer

Cecile Proust-Lima

Last Published

June 26th, 2019

Functions in lcmm (1.8.1)

StandardMethods

Standard methods for estimated models

Estimation of joint latent class models for longitudinal and time-to-event data

Simulated dataset for hlme function

Predicted cumulative incidence of event according to a profile of covariates

Estimates, standard errors and Wald test for the parameters of the variance-covariance matrix of the random effects.

Variance-covariance of the estimates

Automatic grid search

Marginal predictions of the longitudinal outcome(s) in their natural scale from lcmm, Jointlcmm or multlcmm objects

Maximum likelihood estimates

For internal use only ...

Estimators of the Expected Prognostic Observed Cross-Entropy (EPOCE) for evaluating predictive accuracy of joint latent class models estimated using Jointlcmm

Estimation of latent class linear mixed models

Difference of expected prognostic cross-entropy (EPOCE) estimators and its 95% tracking interval between two joint latent class models estimated with Jointlcmm

Estimation of extended mixed models using latent classes and latent processes.

Simulated dataset for lcmm and Jointlcmm functions

Individual dynamic predictions from a joint latent class model

Permutation of the latent classes

Plot of a fitted model

Estimation of mutlivariate mixed-effect models and multivariate latent class mixed-effect models for multivariate longitudinal outcomes of possibly multiple types (continuous Gaussian, continuous non-Gaussian - curvilinear) that measure the same underlying latent process.

Plot of predicted cumulative incidences according to a profile of covariates

Longitudinal data on cognitive and physical aging in the elderly

Plot of individual dynamic predictions

Conditional predictions of a lcmm, multlcmm or Jointlcmm object in the natural scale of the longitudinal outcome(s) for specified latent process values.

Marginal predictions (possibly class-specific) of a hlme, lcmm, multlcmm or Jointlcmm object in the natural scale of the longitudinal outcome(s) for a specified profile of covariates.

Multivariate Wald Test

Posterior classification stemmed from a hlme, lcmm, multlcmm or Jointlcmm estimation

Estimation of mixed-effect models and latent class mixed-effect models for different types of outcomes (continuous Gaussian, continuous non-Gaussian or ordinal)

Estimation of multi-process joint latent class mixed models

Cross classifications

Plot of predicted trajectories and link functions

Confidence intervals for the estimated link functions from lcmm, Jointlcmm and multlcmm

Class-specific marginal predictions in the latent process scale for lcmm, Jointlcmm and multlcmm objects

Brief summary of a hlme, lcmm, Jointlcmm,multlcmm, epoce or Diffepoce objects

Summary of a hlme, lcmm, Jointlcmm, multlcmm, epoce or Diffepoce objects

Summary of models

Percentage of variance explained by the (latent class) linear mixed model regression