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lcmm (version 1.7.3.0)

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

1.7.3.0

License

GPL (>= 2.0)

Maintainer

Cecile Proust-Lima

Last Published

October 23rd, 2015

Functions in lcmm (1.7.3.0)

Plot of a fitted model

Plot of predicted cumulative incidences according to a profile of covariates

Plot of predicted trajectories and link functions

Longitudinal data on cognitive and physical aging in the elderly

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.

Multivariate Wald Test

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

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

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

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

For internal use only ...

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

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

Predicted values of a hlme, lcmm, multlcmm or Jointlcmm object in the natural scale of the longitudinal outcome(s) for a specified profile of covariates.

Simulated dataset for lcmm and Jointlcmm functions

Computation of individual dynamic predictions from a joint latent class model

plot.pred.accuracy

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

Summary of models

Predicted cumulative incidence of event according to a profile of covariates

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

Plot of individual dynamic predictions

Variance-covariance of the estimates

Simulated dataset for hlme function

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

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

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

Estimation of latent class linear mixed models

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

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