lcmm (version 1.5.7)

hlme: Estimation of latent class linear mixed models

Description

This function fits a latent class linear mixed model (LCLMM) also known as growth mixture model or heterogeneous linear mixed model. The LCLMM consists in assuming that the population is divided in a finite number of latent classes; each latent class is characterised by a specific mean trajectory which described by a class-specific linear mixed model. Both the latent class membership and the trajectory can be explained according to covariates. This model is limited to a Gaussian outcome.

Usage

hlme(fixed, mixture, random, subject, classmb, ng = 1, idiag = FALSE, 
 nwg = FALSE, data, B,convB=0.0001,convL=0.0001,convG=0.0001,prior,
 maxiter=500, subset=NULL, na.action=1)

Arguments

fixed

a two-sided linear formula object for the fixed-effects in the linear mixed model. The response outcome is on the left of ~ and the covariates are separated by + on the right of the ~. By default, an intercept is in

mixture

a one-sided formula object for the class-specific fixed effects in the linear mixed model (to specify only for a number of latent classes greater than 1). Among the list of covariates included in fixed, the covariates with class-specific reg

random

an optional one-sided formula for the random-effects in the linear mixed model. Covariates with a random-effect are separated by +. By default, an intercept is included. If no intercept, -1 should be the first term included.

subject

name of the covariate representing the grouping structure.

classmb

an optional one-sided formula describing the covariates in the class-membership multinomial logistic model. Covariates included are separated by +. No intercept should be included in this formula.

ng

number of latent classes considered. If ng=1 no mixture nor classmb should be specified. If ng>1, mixture is required.

idiag

optional logical for the variance-covariance structure of the random-effects. If FALSE, a non structured matrix of variance-covariance is considered (by default). If TRUE a diagonal matrix of variance-covariance is considered.

nwg

optional logical of class-specific variance-covariance of the random-effects. If FALSE the variance-covariance matrix is common over latent classes (by default). If TRUE a class-specific proportional parameter multiplies the vari

data

optional data frame containing the variables named in fixed, mixture, random, classmb and subject.

B

an optional vector containing the initial values for the parameters. The order in which the parameters are included is detailed in details section. If no vector is specified, a preliminary analysis involving the estimation of a standard line

convB

optional threshold for the convergence criterion based on the parameter stability. By default, convB=0.0001.

convL

optional threshold for the convergence criterion based on the log-likelihood stability. By default, convL=0.0001.

convG

optional threshold for the convergence criterion based on the derivatives. By default, convG=0.0001.

prior

name of the covariate containing the prior on the latent class membership. The covariate should be an integer with values in 0,1,...,ng. When there is no prior, the value should be 0. When there is a prior for the subject, the value should be the number o

maxiter

optional maximum number of iterations for the Marquardt iterative algorithm. By default, maxiter=300.

subset

a specification of the rows to be used: defaults to all rows. This can be any valid indexing vector for the rows of data or if that is not supplied, a data frame made up of the variable used in formula.

na.action

Integer indicating how NAs are managed. The default is 1 for 'na.omit'. The alternative is 2 for 'na.fail'. Other options such as 'na.pass' or 'na.exclude' are not implemented in the current version.

Value

• The list returned is:
• ns
  number of grouping units in the dataset
• ng
  number of latent classes
• loglik
  log-likelihood of the model
• best
  vector of parameter estimates in the same order as specified in B and detailed in section details
• V
  vector containing the upper triangle matrix of variance-covariance estimates of Best with exception for variance-covariance parameters of the random-effects for which V contains the variance-covariance estimates of the Cholesky transformed parameters displayed in cholesky
• gconv
  vector of convergence criteria: 1. on the parameters, 2. on the likelihood, 3. on the derivatives
• conv
  status of convergence: =1 if the convergence criteria were satisfied, =2 if the maximum number of iterations was reached, =4 or 5 if a problem occured during optimisation
• call
  the matched call
• niter
  number of Marquardt iterations
• dataset
  dataset
• N
  internal information used in related functions
• idiag
  internal information used in related functions
• pred
  table of individual predictions and residuals; it includes marginal predictions (pred_m), marginal residuals (resid_m), subject-specific predictions (pred_ss) and subject-specific residuals (resid_ss) averaged over classes, the observation (obs) and finally the class-specific marginal and subject-specific predictions (with the number of the latent class: pred_m_1,pred_m_2,...,pred_ss_1,pred_ss_2,...)
• pprob
  table of posterior classification and posterior individual class-membership probabilities
• Xnames
  list of covariates included in the model
• predRE
  table containing individual predictions of the random-effects : a column per random-effect, a line per subject
• cholesky
  vector containing the estimates of the Cholesky transformed parameters of the variance-covariance matrix of the random-effects

Details

The parameters in the vector of initial values B or in the vector of maximum likelihood estimates best are included in the following order: (1) ng-1 parameters are required for intercepts in the latent class membership model, and if covariates are included in classmb, ng-1 paramaters should be entered for each one; (2) for all covariates in fixed, one parameter is required if the covariate is not in mixture, ng paramaters are required if the covariate is also in mixture; (3) the variance of each random-effect specified in random (including the intercept) if idiag=TRUE and the inferior triangular variance-covariance matrix of all the random-effects if idiag=FALSE; (4) only if nwg=TRUE, ng-1 parameters for class-specific proportional coefficients for the variance covariance matrix of the random-effects; (5) the variance of the residual error. We understand that it can be difficult to enter the correct number of parameters in B at the first place. So we recommend to run the program without specifying the initial vector B even if this model does not converge. As the final vector best has exactly the same structure as B (even when the program stops without convergence), it will help defining a satisfying vector of initial values B for next runs.

Some caution should be made when using the program:

(1) As the log-likelihood of a latent class model can have multiple maxima, a careful choice of the initial values is crucial for ensuring convergence toward the global maximum. The program can be run without entering the vector of initial values (see point 2). However, we recommend to systematically enter initial values in B and try different sets of initial values.

(2) The automatic choice of initial values we provide requires the estimation of a preliminary linear mixed model. The user should be aware that first, this preliminary analysis can take time for large datatsets and second, that the generated initial values can be very not likely and even may converge slowly to a local maximum. This is a reason why specification of initial values in B should be preferred.

(4) Convergence criteria are very strict as they are based on derivatives of the log-likelihood in addition to the parameter and log-likelihood stability. In some cases, the program may not converge and reach the maximum number of iterations fixed at 100. In this case, the user should check that parameter estimates at the last iteration are not on the boundaries of the parameter space. If the parameters are on the boundaries of the parameter space, the identifiability of the model should be assessed. If not, the program should be run again with other initial values, with a higher maximum number of iterations or less strict convergence tolerances.

References

Verbeke G and Lesaffre E (1996). A linear mixed-effects model with heterogeneity in the random-effects population. Journal of the American Statistical Association 91, 217-21

Muthen B and Shedden K (1999). Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics 55, 463-9

Proust C and Jacqmin-Gadda H (2005). Estimation of linear mixed models with a mixture of distribution for the random-effects. Computer Methods Programs Biomedicine 78, 165-73

See Also

postprob, plot.postprob, summary, plot.predict

Examples

##### Example of a latent class model estimated for a varying number
# of latent classes: 
# The model includes a subject- (ID) and class-specific linear 
# trend (intercept and Time in fixed, random and mixture components) and
# a common effect of X1 and its interaction with time over classes 
# (in fixed). 
# The random-effects are assumed independent (idiag=TRUE). The variance
# of the random intercept and slope are assumed to be equal 
# over classes (nwg=F).
# The covariate X3 predicts the class membership (in classmb). 
# !CAUTION: for illustration, only default initial values where used but 
# other sets of initial values should be tried to ensure convergence towards
# the global maximum.

data(data_hlme)

### homogeneous linear mixed model (standard linear mixed model) 
### with independent random-effects
m1<-hlme(Y~Time*X1,random=~Time,subject='ID',ng=1,idiag=TRUE,
data=data_hlme)
summary(m1)
### latent class linear mixed model with 2 classes
m2<-hlme(Y~Time*X1,mixture=~Time,random=~Time,classmb=~X2+X3,
subject='ID',ng=2,data=data_hlme)
m2
summary(m2)
postprob(m2)
### same model as m2 with the vector of initial values specified
m3<-hlme(Y~Time*X1,mixture=~Time,random=~Time,classmb=~X2+X3,
subject='ID',ng=2,data=data_hlme,B=c(0,0,0,30,25,0,-1,0,0,5,0,1,1))
m3
summary(m3)

## plot of predicted trajectories using some newdata
newdata<-data.frame(Time=seq(0,5,length=100),
X1=rep(0,100),X2=rep(0,100),X3=rep(0,100))
plot.predict(m3,newdata,"Time","right",bty="l")