ranef: Random Effects Estimates for Joint Models

Description

Extracts the random effects estimates from a fitted joint model.

Usage

# S3 method for 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 \(\{- \partial^2 \log p(b_i | T_i, \delta_i, y_i) / \partial b_i^\top \partial b_i \}^{-1}\).

…

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".

References

Rizopoulos, D. (2012) Joint Models for Longitudinal and Time-to-Event Data: with Applications in R. Boca Raton: Chapman and Hall/CRC.

Examples

Run this code

# 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)
# }