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.

1 | ```
marglogLik(object, newdata, idVar = "id", method = "BFGS", control = NULL)
``` |

`object` |
an object inheriting from class |

`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 |

`idVar` |
the name of the variable in |

`method` |
the |

`control` |
the |

a numeric vector of marginal log-likelihood contributions.

Dimitris Rizopoulos d.rizopoulos@erasmusmc.nl

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
## 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)
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

All documentation is copyright its authors; we didn't write any of that.