The function provides the percentage of variance explained by the (latent class) linear mixed regression in a model estimated with hlme, lcmm, multlcmm or Jointlcmm.
This function produces different plots (residuals, goodness-of-fit, estimated link functions, estimated baseline risk/survival and posterior probabilities distributions) of a fitted object of class hlme, lcmm, multlcmm or Jointlcmm.
Functions for the estimation of latent class mixed models (LCMM), joint latent class mixed models (JLCM) and mixed models for curvilinear and ordinal univariate and multivariate longitudinal outcomes (with or without latent classes of trajectory). All the models are estimated in a maximum likelihood framework using an iterative algorithm. The package also provides various post fit functions.
This function displays the predicted cause-specific cumulative incidences derived from a joint latent class model according to a profile of covariates.
The function computes the marginal predictions of the longitudinal outcome(s) in their natural scale on the individual data used for the estimation from lcmm, Jointlcmm or multlcmm objects.
This function computes the difference of 2 EPOCE estimates (CVPOL or MPOL) and its 95% tracking interval between two joint latent class models estimated using Jointlcmm and evaluated using epoce function. Difference in CVPOL is computed when the EPOCE was previously estimated on the same dataset as used for estimation (using an approximated cross-validation), and difference in MPOL is computed when the EPOCE was previously estimated on an external dataset.
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