externVar to perform a secondary regression analysis after the estimation of a primary latent class modelpprior in hlme, lcmm, multlcmm and Jointlcmm to fix the probability to belong to each latent classJoint latent class model with JointlcmmMultivariate latent class model with mpjlcmmpprior in the hlme functioncomputeDiscrete in the lcmm functionmpjlcmm can be used with a mix of hlme/lcmm/multlcmm objectssummarytable and summaryplot implement two versions of ICL criterionlevels in all estimating functionsvarRE in hlmepermut, cuminc, VarCov, coef, vcov functions are available for mpjlcmm objectsmpjlcmm, especially with competing risksposfix and partialH simultaneouslypredictClass and predictRE when using splineshlme function has now a pprior argumentmpjlcmm function can be used without a time-to-event modelsummary functions now shorten the parameters namesmpjlcmm when no random effect is includedJointlcmm with Weibull hazards and competing riskspermut when used on Jointlcmm objects with competing risksmultlcmm modelsDynamic IRT with multlcmmsimulate to simulate a dataset from a hlme, lcmm, multlcmm or Jointlcmm modelItemInfo and plot.ItemInfo to compute and plot Fisher information for ordinal outcomesvar.time in the hlme, lcmm, multlcmm and Jointlcmm functions (used in plot(, which="fit"); issue #91)permut function (transformation parameters were not updated)gridsearch function now checks that the initial model converged (ie minit$conv=1)fixef and ranef function are now imported from the nlme packagepredictClass, predictRE and summaryplotsummaryplotrmvnorm in multlcmm to generate random initial valuesmaxiter is used in the estimation of the final model in gridsearchcuminc without covariatessubject with tibblespredictY with hlme object when the dataset is named "x"update function when the model has unestimated parameters (posfix)hlme when posterior probabilities are NAplot with option which="fit" (observations at the maximum time measurement where not systematically included)mpjlcmm functionJointlcmm with prior when there are missing datampjlcmm : initial values were badly modified (with at least 3 dimensions)predictY with median=TRUEgridsearch function. Thanks to Raphael Peter for his suggestion.condRE_Y option in predictYcondmedian options in predictYJointlcmm, multlcmm and mpjlcmm when prior is specifiedVarExpl with models including BM or ARupdate.mpjlcmm (variance matrix was not correct)hlmempjlcmm for estimating joint latent class models with multiple markers and/or latent processesmpjlcmm objectspermut and xclasssubject must be numericlcmm with priorJointlcmm with infinite score testdynpred with TimeDepVarThe 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.
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.
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
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.
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.
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
splines link function and an outcome with minimum value not at 0The 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
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
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=).