jm.reffects: Posterior means/medians of the random effects from a joint...

In sa4khan/AFTjmr: Recurrent event analysis and joint modeling using accelerated failure time (AFT) models

Description

Returns posterior means/medians of the random effects. Note that the random effects are shared between the longitudinal and survival components, and the link between these two processes via the random effects is commonly known as latent association. The formulation of the joint model is is described in jmreg.aft.

Usage

jm.reffects(bayes.fit, posterior.mean = TRUE)

Arguments

bayes.fit	fit of the joint model using jmreg.aft.
posterior.mean	returns posterior means if TRUE, and posterior medians if FALSE.

Details

The random effects are monitored in MCMC simulations.

Value

Returns posterior means/medians of the random effects.

See Also

jmreg.aft, jm.resid, jm.summary

Examples

  data(pbc.long)
  data(pbc.surv)
  agec<-pbc.surv$age-mean(pbc.surv$age)
  pbc.surv0<-data.frame(pbc.surv,agec=agec)
# use natural splines
  lme.fit<-lme(log(bilirubin)~drug+ns(futime,2),data=pbc.long,
         random=~ns(futime,2)|id)
  surv.fit<-coxph(Surv(st,status2)~drug*agec,data=pbc.surv0,x=TRUE)
# use rand.model="ns"
  jmfit.w<-jmreg.aft(surv.fit=surv.fit,lme.fit=lme.fit,
          surv.model="weibull",rand.model="ns",timevar="futime",
          n.chain=2,n.adapt = 5000, n.burn = 15000, 
          n.iter = 150000, n.thin = 5)
  jm.reffects(jmfit.w)
  jm.reffects(jmfit.w,posterior.mean=FALSE)

sa4khan/AFTjmr documentation built on March 12, 2020, 1:24 a.m.