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lcmm (version 1.6.6)

Estimation of Extended Mixed Models Using Latent Classes and Latent Processes

Description

Functions for the 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

1.6.6

License

GPL (>= 2.0)

Maintainer

Last Published

September 11th, 2014

Functions in lcmm (1.6.6)

Simulated dataset for hlme function

Plot of individual dynamic predictions

Histograms of the posterior class-membership probabilities

Computation of individual dynamic predictions from a joint latent class model

Simulated dataset for lcmm and Jointlcmm functions

Variance-covariance of the estimates

plot.baselinerisk

Plot of the class-specific baseline risk functions estimated from a joint latent class mixed model.

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

Confidence intervals for the estimated link functions from lcmm and multlcmm

plot.linkfunction

Plot of the estimated transformation between the outcome and the underlying latent process

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

Plots of marginal and subject-specific residuals

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)

For internal use only ...

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

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

Plot of class-specific marginal predictions for the longitudinal outcome

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

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

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

plot.pred.accuracy

Longitudinal data on cognitive and physical aging in the elderly

Plot of the class-specific event-free probabilities (survival functions) estimated from a joint latent class mixed model

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

Estimation of latent class linear mixed models

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

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.