jm.WAIC: Computes WAIC for Bayesian fit of the joint model
In sa4khan/AFTjmr: Recurrent event analysis and joint modeling using accelerated failure time (AFT) models

Description Usage Arguments Details Value Author(s) References See Also Examples

Returns WAIC.

1	jm.WAIC(bayes.fit)

bayes.fit

a fit of the joint model using jmreg.aft.

WAIC is computed using -2*(lppd - pwaic), where lppd = log pointwise predictive density, and pwaic = effective number of parameters. The effective number of parameters is estimated using the variance of individual terms in the log predictive density summed over the data points. See Gelman et al. (2014) for details.

Returns lppd, pwaic (effective number of parameters), and WAIC.

Shahedul Khan <khan@math.usask.ca>

Gelman A, Hwang J, and Vehtari A, Understanding predictive information criteria for bayesian models, Statistics and Computing 24: 997-1016, 2014.

jmreg.aft, jm.DIC, jm.summary

  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.WAIC(jmfit.w)