jm coda Methods | R Documentation |
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', 'custom'), grid = FALSE, gridrows = 3, gridcols = 1, custom_theme = NULL, ...) 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', 'custom'), grid = FALSE, gridrows = 3, gridcols = 1, custom_theme = NULL, ...) 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 |
custom_theme |
A named character vector with elements equal to the number of chains ( |
... |
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
jm
# 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) ggtraceplot(fit_jm, parm = "betas", custom_theme = c('1' = 'black')) # trace plot for the fixed effects in the linear mixed submodels ggdensityplot(fit_jm, parm = "betas") ggdensityplot(fit_jm, parm = "betas", grid = TRUE) ggdensityplot(fit_jm, parm = "betas", custom_theme = c('1' = 'black'))
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