traceplot.Rd
Creates a set of traceplots from the MCMC sample of an object of class "JointAI".
traceplot(object, ...) # S3 method for mcmc.list traceplot(object, start = NULL, end = NULL, thin = NULL, ...) # S3 method for JointAI traceplot( object, start = NULL, end = NULL, thin = NULL, subset = c(analysis_main = TRUE), exclude_chains = NULL, nrow = NULL, ncol = NULL, keep_aux = FALSE, use_ggplot = FALSE, warn = TRUE, mess = TRUE, ... )
object  object inheriting from class 'JointAI' 

...  Arguments passed on to

start  the first iteration of interest (see 
end  the last iteration of interest (see 
thin  thinning interval (see 
subset  subset of parameters/variables/nodes (columns in the MCMC sample).
Uses the same logic as the argument 
exclude_chains  optional vector of the index numbers of chains that should be excluded 
nrow  optional number of rows and columns in the plot layout; automatically chosen if unspecified 
ncol  optional number of rows and columns in the plot layout; automatically chosen if unspecified 
keep_aux  logical; Should constant effects of auxiliary variables be kept in the output? 
use_ggplot  logical; Should ggplot be used instead of the base graphics? 
warn  logical; should warnings be given? Default is

mess  logical; should messages be given? Default is

summary.JointAI
, lme_imp
, glm_imp
,
lm_imp
, densplot
The vignette Parameter Selection
contains some examples how to specify the parameter subset
.
# fit a JointAI model mod < lm_imp(y ~ C1 + C2 + M1, data = wideDF, n.iter = 100) # Example 1: simple traceplot traceplot(mod)# Example 2: ggplot version of traceplot traceplot(mod, use_ggplot = TRUE)# Example 5: changing how the ggplot version looks (using standard ggplot syntax) library(ggplot2) traceplot(mod, use_ggplot = TRUE) + theme(legend.position = 'botto') + xlab('iteration') + ylab('value') + scale_color_discrete(name = 'chain')