Calculates Marginal Subject-specific Log-Likelihood Contributions
This function computes marginal subject-specific log-likelihood contributions based on a fitted joint model. The marginalization is done with respect to both the random effects and the parameters using a Laplace approximation.
marglogLik(object, newdata, idVar = "id", method = "BFGS", control = NULL)
object: an object inheriting from class JMBayes.newdata: a data frame that contains the longitudinal and covariate information for the subjects for which prediction of survival probabilities is required. The names of the variables in this data frame must be the same as in the data frames that were used to fit the linear mixed effects model (using lme()) and the survival model (using coxph()) that were supplied as the two first argument of jointModelBayes. In addition, this data frame should contain a variable that identifies the different subjects (see also argument idVar).idVar: the name of the variable in newdata that identifies the different subjects.method: the method argument of optim().control: the control argument of optim().a numeric vector of marginal log-likelihood contributions.
Dimitris Rizopoulos d.rizopoulos@erasmusmc.nl
## Not run: # we construct the composite event indicator (transplantation or death) pbc2$status2 <- as.numeric(pbc2$status != "alive") pbc2.id$status2 <- as.numeric(pbc2.id$status != "alive") # we fit a joint model using splines for the subject-specific # longitudinal trajectories and a spline-approximated baseline # risk function lmeFit <- lme(log(serBilir) ~ ns(year, 2), data = pbc2, random = ~ ns(year, 2) | id) survFit <- coxph(Surv(years, status2) ~ drug, data = pbc2.id, x = TRUE) jointFit <- jointModelBayes(lmeFit, survFit, timeVar = "year") # we compute marginal log-likelihood contribution for Subject 2 ND <- pbc2[pbc2$id == 2, ] # the data of Subject 2 marglogLik(jointFit, newdata = ND) ## End(Not run)