jm.summary: Summary of a joint model fit
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

Produce a summary of the fit, including density, trace and autocorrelation plots.

1 2	jm.summary(bayes.fit, trace.plot = FALSE, density.plot = FALSE, autocorr.plot = FALSE)

`bayes.fit`	fit of the joint model using `jmreg.aft`.
`trace.plot`	produce trace plots if TRUE (default is FALSE).
`density.plot`	produce density plots if TRUE (default is FALSE).
`autocorr.plot`	produce autocorrelation plots if TRUE (default is FALSE).

The coda package (Plummer et al., 2006) is used to summarize and plot the output from MCMC simulations. It also includes Gelman-Rubin statistic for convergence diagnostic.

Returns posterior summaries of the parameters, including posterior mean, median, standard deviation, 0.025 and 0.975 quantiles, and Gelman-Rubin statistic values. It also returns some basic information about the model, computational time, and MCMC simulation setup.

Shahedul Khan <khan@math.usask.ca>

Plummer M, Best N, Cowles K, and Vines K, CODA: Convergence diagnosis and output analysis for MCMC, R News, 6: 7-11, 2006.

jm.DIC, jmreg.aft, jm.WAIC

Consider the pbc data from package JM.
  library(JM)
  data(aids.id)
  data(aids)
  surv.fit<-coxph(Surv(Time,death)~drug+gender+prevOI+AZT,
          data=aids.id,x=TRUE)
  lme.fit<-lme(CD4~obstime+obstime:drug+gender+prevOI+AZT,
         random=~obstime|patient,data=aids)
  jmfit.ew<-jmreg.aft(surv.fit=surv.fit,lme.fit=lme.fit,
         surv.model="eweibull",rand.model="simple",timevar="obstime",
         n.chain=2,n.adapt = 5000, n.burn = 15000, 
         n.iter = 150000, n.thin = 5)
  jm.summary(jmfit.ew,trace.plot=TRUE,density.plot=TRUE)