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, ...)

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".

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 ${- \partial^{2} \log p (b_{i} | T_{i}, δ_{i}, y_{i}) / \partial b_{i}^{⊤} \partial b_{i}}^{- 1}$ .
...: additional arguments; currently none is used.

Author

Dimitris Rizopoulos d.rizopoulos@erasmusmc.nl

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

if (FALSE) {
# 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)
}

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