plot {JMbayes} | R Documentation |
MCMC Diagnostics for Joint Models
Description
Produces MCMC diagnostics plots.
Usage
## S3 method for class 'JMbayes'
plot(x, which = c("trace", "autocorr", "density", "CPO", "weightFun"),
param = c("betas", "sigma", "D", "gammas", "alphas", "Dalphas",
"shapes", "Bs.gammas", "tauBs"), ask = TRUE, max.t = NULL,
from = 0, ...)
Arguments
x |
an object inheriting from class |
which |
which types of plots to produce. |
param |
for which parameter to produce the MCMC diagnostic plots; default is for all parameters. |
ask |
logical, if |
max.t |
numeric scalar; up to which time point to plot the weight function, default is up to the third quantile of the observed event times. |
from |
numeric scalar; from which time point to start plotting the weight function, default is zero. |
... |
additional arguments; currently none is used. |
Author(s)
Dimitris Rizopoulos d.rizopoulos@erasmusmc.nl
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
Examples
## Not run:
# linear mixed model fit
fitLME <- lme(log(serBilir) ~ drug * year, random = ~ 1 | id, data = pbc2)
# survival regression fit
fitSURV <- coxph(Surv(years, status2) ~ drug, data = pbc2.id, x = TRUE)
# joint model fit, under the (default) Weibull model
fitJOINT <- jointModelBayes(fitLME, fitSURV, timeVar = "year")
plot(fitJOINT)
## End(Not run)
[Package JMbayes version 0.8-85 Index]