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.
A detailed companion paper is available in Journal of Statistical Software:
Proust-Lima C, Philipps V, Liquet B. Estimation of Extended Mixed Models Using Latent Classes and Latent Processes: The R Package lcmm. Journal of Statistical Software, Articles. 2017;78(2):1-56.
https://www.jstatsoft.org/article/view/v078i02
Changes in Version 1.7.9 :
Changes in Version 1.7.8 :
Changes in Version 1.7.6 (2016-12-12):
Changes in Version 1.7.5 (2016-03-15):
Changes in Version 1.7.4 (2015-12-26):
The package uses lazydata to automatically load the datasets of the package.
'jlcmm' and 'mlcmm' are shortcuts for functions 'Jointlcmm' and 'multlcmm', respectively.
Function 'gridsearch' provides an automatic grid of departures for reducing the odds of converging towards a local maximum.
Initial values can be randomly generated from a model with 1 class (called m1 in next example) with option B=random(m1) in hlme, lcmm, multlcmm and Jointlcmm.
Changes in Version 1.7.3.0 (2015-10-23):
Functions 'hlme', 'lcmm', 'multlcmm', 'Jointlcmm' now include a posfix option to specify parameters that should not be estimated.
Functions 'lcmm', 'multlcmm', 'Jointlcmm' now include a partialH option to restrict the computation of the inverse of the Hessian matrix to a submatrix
Functions 'hlme', 'lcmm', 'multlcmm', 'Jointlcmm' now allow optional vector B to be an estimated model (with G=1) to reduce calculation time of initial values.
Bug identified and solved in calculation of subject-specific predictions in 'hlme', 'lcmm', 'multlcmm' and 'Jointlcmm' when cor is not NULL.
Bug identified and solved in the calculation of confidence bands for individual dynamic predictions in dynpred with draws=T.
Bug identified and solved in the calculation of the explained variance for multlcmm objects when cor is not NULL.
Changes in Version 1.7.1 & 1.7.2 (2015-02-27):
Function plot now includes a which="fit" option to plot observed and predicted trajectories stemming from a hlme, lcmm, Jointlcmm or multlcmm object.
Function 'predictlink' replaces deprecated function 'link.confint'
Function 'plot' gathers deprecated functions 'plot.linkfunction', 'plot.baselinerisk', 'plot.survival', 'plot.fit' together
Changes in Version 1.7.0 (2015-02-13):
The function 'Jointlcmm' now allows competing risks data for the survival part and is also available for non-Gaussian longitudinal data. All existing methods for Jointlcmm objects (except EPOCE and Diffepoce functions) are adapted to the new framework.
Functions 'link.confint', 'plot.linkfunction', 'predictL' are now available for Jointlcmm objects.
The new functions 'incidcum' and 'plot.incidcum' respectively compute and plot the cumulative incidence associated to each competing event for Jointlcmm object.
The new function 'fitY' computes the marginal predicted values of longitudinal outcomes in their natural scale for lcmm or multlcmm objects.
Bug identified and solved in 'dynpred' function when used with a joint model assuming proportional hazards between latent classes.
The Makevars file now allows compilation of the package with parallel make.
Changes in Version 1.6.5 & 1.6.6 (2014-09-10):
Changes in Version 1.6.4 (2014-04-11):
The new functions 'dynpred' and 'plot.dynpred' respectively compute and plot individual dynamic predictions obtained from a joint latent class model estimated by Jointlcmm.
The new function 'VarCovRE' computes the standard errors of the parameters of variance-covariance of the random effects for a hlme, lcmm, Jointlcmm or multlcmm object
The new function 'WaldMult' computes multivariate Wald tests and Wald tests for combinations of parameters from hlme, lcmm, Jointlcmm or multlcmm object
The new function 'VarExpl' computes the percentages of variance explained by the linear regression for a hlme, lcmm, Jointlclmm or multlcmm object
The new functions 'estimates' and 'VarCov' get respectively all parameters estimated and their variance-covariance matrix for a hlme, lcmm, Jointlcmm or multlcmm object
Function 'summary' now returns the table containing the results about the fixed effects in the longitudinal model
All plots consider now the ... options
Functions plot.linkfunction and plot.predict have now an add argument
Function multlcmm now allows "splines" or "Splines" specification for the link functions
Functions 'lcmm' and 'multlcmm' now compute the transformations even if the maximum number of iterations is reached without convergence
bug identified and solved in multlcmm when the response variables are not integers
bug identified and solved in multlcmm when using contrast
bug identified and solved in plot.linkfunction for the y axes positions
bug identified and solved in hlme, lcmm, Jointlcmm and multlcmm when including interactions in 'mixture'.
Changes in Version 1.6.2 (2013-03-06):
The new function 'multlcmm' now estimates latent process mixed models for multivariate curvilinear longitudinal outcomes (with link functions: linear, beta or splines). Various post-fit computation and output functions are also available including plot.linkfunction, predictY, predictL, etc
All the functions hlme, lcmm, Jointlcmm include a 'cor' option for including a brownian motion or a first-order autoregressive error process in addition to the independent errors of measurement
bug identified and solved in predictL, predictY and plot.predict when used with factor covariate
Changes in Version 1.5.8 (2012-10-01):
Changes in Version 1.5.7 (2012-07-24):
The function 'predictY' now computes the predicted values (possibly class-specific) of the longitudinal outcome not only from a lcmm object but also from a hlme or a Jointlcmm object for a specified profile of covariates.
bug identified and solved in predictY.lcmm when used with a 'threshold' link function and a Monte Carlo method
Changes in Version 1.5.6 (2012-07-16):
missing data handled in hlme, lcmm and Jointlcmm using 'na.action' with attributes 1 for 'na.omit' or 2 for 'na.fail'
The new function 'predictY.lcmm' computes predicted values of a lcmm object in the natural outcome scale for a specified profile of covariates, and also provides confidence bands using a Monte Carlo method.
bugs in epoce computation solved (with splines baseline risk function, and/or NaN values under solaris system)
bug identified and solved in summary functions regarding the labels of covariate effects in peculiar cases
Changes in Version 1.5.2 (2012-04-06):
improved variable specification in the estimating functions Jointlcmm, lcmm and hlme with
computation of the predictive accuracy measure EPOCE from a Jointlcmm object either on the training data or on external data (post-fit functions epoce and Diffepoce)
for discrete outcomes, lcmm function now computates the posterior discrete log-likelihood and the universal approximate cross-validation criterion (UACV)
Jointlcmm now includes two parameterizations of I-splines and piecewise-constant baseline risks functions to ensure positive risks: either log/exp or sqrt/square (option logscale=).