JM (version 1.5-2)

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

...

additional arguments; currently none is used.

Author

Dimitris Rizopoulos

References

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

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)
}