Description Usage Arguments Details Value Author(s) References See Also Examples

Computes the log-likelihood for a fitted joint model.

1 2 3 |

`object` |
an object inheriting from class |

`thetas` |
a list with values for the joint model's parameters. This should have the same structure as
the |

`b` |
a numeric matrix with random effects value. This should have the same structure as
the |

`priors` |
logical, if |

`marginal.b` |
logical, if |

`marginal.thetas` |
logical, if |

`full.Laplace` |
logical, if |

`useModes` |
logical, if |

`...` |
extra arguments; currently none is used. |

Let *y_i* denote the vectors of longitudinal responses, *T_i* the observed event time, and *δ_i*
the event indicator for subject *i* (*i = 1, …, n*). Let also *p(y_i | b_i; θ)* denote the probability
density function (pdf) for the linear mixed model, *p(T_i, δ_i | b_i; θ)* the pdf for the survival submodel, and
*p(b_i; θ)* the multivariate normal pdf for the random effects, where *θ* denotes the full parameter vector. Then,
if `priors = TRUE`

, and `marginal.b = TRUE`

, function `logLik()`

computes

*\log \int p(y_i | b_i; θ) p(T_i, δ_i | b_i; θ) p(b_i; θ) db_i + \log p(θ),*

where *p(θ)* denotes the prior distribution for the parameters. If `priors = FALSE`

the prior is excluded from the
computation, i.e.,

*\log \int p(y_i | b_i; θ) p(T_i, δ_i | b_i; θ) p(b_i; θ) db_i,*

and when
`marginal.b = FALSE`

, then the conditional on the random effects log-likelihood is computed, i.e.,

*\log p(y_i | b_i; θ) + \log p(T_i, δ_i | b_i; θ) + \log p(b_i; θ) + \log p(θ),*

when
`priors = TRUE`

and

*\log p(y_i | b_i; θ) + \log p(T_i, δ_i | b_i; θ) + \log p(b_i; θ),*

when `priors = FALSE`

.

a numeric scalar of class `logLik`

with the value of the log-likelihood. It also has
the attributes `df`

the number of parameter (excluding the random effects), and `nobs`

the number of subjects.

Dimitris Rizopoulos d.rizopoulos@erasmusmc.nl

Rizopoulos, D., Hatfield, L., Carlin, B. and Takkenberg, J. (2014). Combining dynamic predictions from joint models for longitudinal and time-to-event data using Bayesian model averaging. *Journal of the American Statistical Association*. to appear.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
## Not run:
lmeFit <- lme(log(serBilir) ~ ns(year, 2), data = pbc2,
random = ~ ns(year, 2) | id)
survFit <- coxph(Surv(years, status2) ~ 1, data = pbc2.id, x = TRUE)
jointFit <- jointModelBayes(lmeFit, survFit, timeVar = "year")
logLik(jointFit)
logLik(jointFit, priors = FALSE)
logLik(jointFit, marginal.b = FALSE)
## End(Not run)
``` |

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