plot: MCMC Diagnostics for Joint Models

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

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 JMbayes.
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 TRUE the user is asked for input, before a new figure is drawn.
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 [e-mail]

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

jointModelBayes

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)

JMbayes documentation built on Jan. 9, 2020, 9:07 a.m.