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=).