Extracts the random effects estimates from a fitted joint model.
Usage
## S3 method for class 'jointModel'
ranef(object, type = c("mean", "mode"), postVar = FALSE, ...)
Arguments
object
an object inheriting from class jointModel.
type
what type of empirical Bayes estimates to compute, the mean of the posterior distribution or
the mode of the posterior distribution.
postVar
logical; if TRUE the variance of the posterior distribution is also returned. When
type == "mode", then this equals {- partial^2 log p(b_i | T_i,
δ_i, y_i) / partial b_i^\top partial b_i }^{-1}.
...
additional arguments; currently none is used.
Value
a numeric matrix with rows denoting the individuals and columns the random effects (e.g., intercepts, slopes, etc.).
If postVar = TRUE, the numeric matrix has an extra attribute “postVar".
Rizopoulos, D. (2012) Joint Models for Longitudinal and Time-to-Event Data: with
Applications in R. Boca Raton: Chapman and Hall/CRC.
See Also
coef.jointModel, fixef.jointModel
Examples
## Not run:
# linear mixed model fit
fitLME <- lme(log(serBilir) ~ drug * year, random = ~ 1 | id, data = pbc2)
# survival regression fit
fitSURV <- survreg(Surv(years, status2) ~ drug, data = pbc2.id, x = TRUE)
# joint model fit, under the (default) Weibull model
fitJOINT <- jointModel(fitLME, fitSURV, timeVar = "year")
ranef(fitJOINT)
## End(Not run)