coef: Estimated Coefficients and Confidence Intervals for Joint...

In drizopoulos/JMbayes: Joint Modeling of Longitudinal and Time-to-Event Data under a Bayesian Approach

Description

Extracts estimated coefficients and confidence intervals from fitted joint models.

Usage

## S3 method for class 'JMbayes'
coef(object, process = c("Longitudinal", "Event"), ...)

## S3 method for class 'JMbayes'
fixef(object, process = c("Longitudinal", "Event"), ...)

## S3 method for class 'JMbayes'
confint(object, parm = c("all", "Longitudinal", "Event"), ...)

Arguments

object	an object inheriting from class JMbayes.
process	for which submodel (i.e., linear mixed model or survival model) to extract the estimated coefficients.
parm	for which submodel (i.e., linear mixed model or survival model) to extract credible intervals.
...	additional arguments; currently none is used.

Details

When process = "Event" both methods return the same output. However, for process = "Longitudinal", the coef() method returns the subject-specific coefficients, whereas fixef() only the fixed effects.

Value

A numeric vector or a matrix of the estimated parameters or confidence intervals for the fitted model.

Author(s)

Dimitris Rizopoulos [e-mail]

See Also

ranef.JMbayes, jointModelBayes

Examples

## Not run: 
# linear mixed model fit
fitLME <- lme(sqrt(CD4) ~ obstime * drug - drug, 
    random = ~ 1 | patient, data = aids)
# cox model fit
fitCOX <- coxph(Surv(Time, death) ~ drug, data = aids.id, x = TRUE)

# joint model fit
fitJOINT <- jointModelBayes(fitLME, fitCOX, 
    timeVar = "obstime")

# fixed effects for the longitudinal process
fixef(fitJOINT)

# fixed effects + random effects estimates for the longitudinal 
# process
coef(fitJOINT)

# fixed effects for the event process
fixef(fitJOINT, process = "Event")
coef(fitJOINT, process = "Event")

## End(Not run)

drizopoulos/JMbayes documentation built on Feb. 2, 2021, 12:34 a.m.