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Presentation

lcmm-package
Estimation of extended mixed models using latent classes and latent processes.

Data

data_hlme
Simulated dataset for hlme function
data_lcmm
Simulated dataset for lcmm and Jointlcmm functions
paquid
Longitudinal data on cognitive and physical aging in the elderly
simdataHADS
Simulated dataset simdataHADS

Estimation

hlme()
Estimation of latent class linear mixed models
lcmm()
Estimation of mixed-effect models and latent class mixed-effect models for different types of outcomes (continuous Gaussian, continuous non-Gaussian or ordinal)
multlcmm() mlcmm()
Estimation of multivariate mixed-effect models and multivariate latent class mixed-effect models for multivariate longitudinal outcomes of possibly multiple types (continuous Gaussian, continuous non-Gaussian/curvilinear, ordinal) that measure the same underlying latent process.
Jointlcmm() jlcmm()
Estimation of joint latent class models for longitudinal and time-to-event data
mpjlcmm()
Estimation of multivariate joint latent class mixed models
externVar()
Estimation of secondary regression models after the estimation of a primary latent class model
gridsearch()
Automatic grid search
loglikhlme() logliklcmm() loglikmultlcmm() loglikJointlcmm() loglikmpjlcmm()
Wrapper to the Fortran subroutines computing the log-likelihood
permut()
Permutation of the latent classes

Extract outputs

estimates()
Maximum likelihood estimates
print(<lcmm>)
Brief summary of a hlme, lcmm, Jointlcmm,multlcmm, epoce or Diffepoce objects
postprob()
Posterior classification stemmed from a hlme, lcmm, multlcmm or Jointlcmm estimation
coef(<hlme>)
Standard methods for estimated models
summary(<lcmm>)
Summary of a hlme, lcmm, Jointlcmm, multlcmm, mpjlcmm, externSurv, externX epoce or Diffepoce objects
summarytable()
Summary of models
update(<mpjlcmm>)
Update the longitudinal submodels
VarCov()
Variance-covariance of the estimates
xclass()
Cross classifications

Predictions and computations

cuminc()
Predicted cumulative incidence of event according to a profile of covariates
Diffepoce()
Difference of expected prognostic cross-entropy (EPOCE) estimators and its 95% tracking interval between two joint latent class models estimated with Jointlcmm
dynpred()
Individual dynamic predictions from a joint latent class model
epoce()
Estimators of the Expected Prognostic Observed Cross-Entropy (EPOCE) for evaluating predictive accuracy of joint latent class models estimated using Jointlcmm
ItemInfo()
Conditional probabilities and item information given specified latent process values for lcmm or multlcmm object with ordinal outcomes.
predictClass()
Posterior classification and class-membership probabilities
predictlink()
Confidence intervals for the estimated link functions from lcmm, Jointlcmm and multlcmm
predictL()
Class-specific marginal predictions in the latent process scale for lcmm, Jointlcmm and multlcmm objects
predictRE()
Predictions of the random-effects
predictY()
Predictions (marginal and possibly subject-specific in some cases) of a hlme, lcmm, multlcmm or Jointlcmm object in the natural scale of the longitudinal outcome(s) computed from a profile of covariates (marginal) or individual data (subject specific in case of hlme).
predictYcond()
Conditional predictions of a lcmm, multlcmm or Jointlcmm object in the natural scale of the longitudinal outcome(s) for specified latent process values.

Plots

plot(<hlme>) plot(<lcmm>) plot(<multlcmm>) plot(<Jointlcmm>) plot(<mpjlcmm>) plot(<externSurv>) plot(<externX>)
Plot of a fitted model
plot(<cuminc>)
Plot of predicted cumulative incidences according to a profile of covariates
plot(<dynpred>)
Plot of individual dynamic predictions
plot(<Diffepoce>) plot(<epoce>)
Plots
plot(<ItemInfo>)
Plot of information functions
plot(<predictL>) plot(<predictY>) plot(<predictYcond>)
Plot of predicted trajectories and link functions
summaryplot()
Summary of models

Evaluation and tests

fitY()
Marginal predictions of the longitudinal outcome(s) in their natural scale from lcmm, Jointlcmm or multlcmm objects
VarExpl()
Percentage of variance explained by the (latent class) linear mixed model regression
VarCovRE()
Estimates, standard errors and Wald test for the parameters of the variance-covariance matrix of the random effects.
WaldMult()
Multivariate Wald Test

Simulation

simulate(<lcmm>)
Data simulation according to models from lcmm package

Internal functions