ranef: Random Effects Estimates for Joint Models

In JM: Joint Modeling of Longitudinal and Survival Data

Random Effects Estimates for Joint Models

Description

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".

Author(s)

Dimitris Rizopoulos [e-mail]

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

## 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)

JM documentation built on Aug. 8, 2022, 5:09 p.m.