Various Methods for Functions from the coda Package
Methods for an object of class "jm" for diagnostic functions.
traceplot(object, ...)
## S3 method for class 'jm'
traceplot(object,
parm = c("all", "betas", "sigmas", "D", "bs_gammas",
"tau_bs_gammas", "gammas", "alphas"), ...)
ggtraceplot(object, ...)
## S3 method for class 'jm'
ggtraceplot(object,
parm = c("all", "betas", "sigmas", "D", "bs_gammas",
"tau_bs_gammas", "gammas", "alphas"),
size = 1, alpha = 0.8,
theme = c('standard', 'catalog', 'metro',
'pastel', 'beach', 'moonlight', 'goo', 'sunset'),
grid = FALSE, gridrows = 3, gridcols = 1, ...)
gelman_diag(object, ...)
## S3 method for class 'jm'
gelman_diag(object,
parm = c("all", "betas", "sigmas", "D", "bs_gammas",
"tau_bs_gammas", "gammas", "alphas"), ...)
densplot(object, ...)
## S3 method for class 'jm'
densplot(object,
parm = c("all", "betas", "sigmas", "D", "bs_gammas",
"tau_bs_gammas", "gammas", "alphas"), ...)
ggdensityplot(object, ...)
## S3 method for class 'jm'
ggdensityplot(object,
parm = c("all", "betas", "sigmas", "D", "bs_gammas",
"tau_bs_gammas", "gammas", "alphas"),
size = 1, alpha = 0.6,
theme = c('standard', 'catalog', 'metro', 'pastel',
'beach', 'moonlight', 'goo', 'sunset'),
grid = FALSE, gridrows = 3, gridcols = 1, ...)
cumuplot(object, ...)
## S3 method for class 'jm'
cumuplot(object,
parm = c("all", "betas", "sigmas", "D", "bs_gammas",
"tau_bs_gammas", "gammas", "alphas"), ...)object |
an object inheriting from class |
parm |
a character string specifying which parameters of the joint model to plot. Possible options are |
size |
the width of the traceplot line in mm. Defaults to 1. |
alpha |
the opacity level of the traceplot line. Defaults to 0.8. |
theme |
a character string specifying the color theme to be used. Possible options are |
grid |
logical; defaults to |
gridrows |
number of rows per page for the grid. Only relevant when using |
gridcols |
number of columns per page for the grid. Only relevant when using |
... |
further arguments passed to the corresponding function from the coda package. |
traceplot()Plots the evolution of the estimated parameter vs. iterations in a fitted joint model.
ggtraceplot()Plots the evolution of the estimated parameter vs. iterations in a fitted joint model using ggplot2.
gelman_diag()Calculates the potential scale reduction factor for the estimated parameters in a fitted joint model, together with the upper confidence limits.
densplot()Plots the density estimate for the estimated parameters in a fitted joint model.
ggdensityplot()Plots the evolution of the estimated parameter vs. iterations in a fitted joint model using ggplot2.
cumuplot()Plots the evolution of the sample quantiles vs. iterations in a fitted joint model.
Dimitris Rizopoulos d.rizopoulos@erasmusmc.nl
# linear mixed model fits
fit_lme1 <- lme(log(serBilir) ~ year:sex + age,
random = ~ year | id, data = pbc2)
fit_lme2 <- lme(prothrombin ~ sex,
random = ~ year | id, data = pbc2)
# cox model fit
fit_cox <- coxph(Surv(years, status2) ~ age, data = pbc2.id)
# joint model fit
fit_jm <- jm(fit_cox, list(fit_lme1, fit_lme2), time_var = "year", n_chains = 1L)
# trace plot for the fixed effects in the linear mixed submodels
traceplot(fit_jm, parm = "betas")
# density plot for the fixed effects in the linear mixed submodels
densplot(fit_jm, parm = "betas")
# cumulative quantile plot for the fixed effects in the linear mixed submodels
cumuplot(fit_jm, parm = "betas")
# trace plot for the fixed effects in the linear mixed submodels
ggtraceplot(fit_jm, parm = "betas")
ggtraceplot(fit_jm, parm = "betas", grid = TRUE)
# trace plot for the fixed effects in the linear mixed submodels
ggdensityplot(fit_jm, parm = "betas")
ggdensityplot(fit_jm, parm = "betas", grid = TRUE)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.