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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