traceplot: Markov chain traceplots

Description

Draw the traceplot corresponding to one or more Markov chains, providing a visual way to inspect sampling behavior and assess mixing across chains and convergence.

Usage


  # S4 method for stanfit
traceplot(object, pars, include = TRUE, unconstrain = FALSE, 
          inc_warmup = FALSE, window = NULL, nrow = NULL, ncol = NULL, ...)

Value

A ggplot object that can be further customized using the ggplot2 package.

Methods

traceplot: signature(object = "stanfit") Plot the sampling paths for all chains.

Arguments

object: An instance of class stanfit.
pars: A character vector of parameter names. Defaults to all parameters or the first 10 parameters (if there are more than 10).
include: Should the parameters given by the pars argument be included (the default) or excluded from the plot? Only relevant if pars is not missing.
inc_warmup: TRUE or FALSE, indicating whether the warmup sample are included in the trace plot; defaults to FALSE.
window: A vector of length 2. Iterations between window[1] and window[2] will be shown in the plot. The default is to show all iterations if inc_warmup is TRUE and all iterations from the sampling period only if inc_warmup is FALSE. If inc_warmup is FALSE the iterations specified in window should not include iterations from the warmup period.
unconstrain: Should parameters be plotted on the unconstrained space? Defaults to FALSE.
nrow,ncol: Passed to facet_wrap.
...: Optional arguments to pass to geom_path (e.g. size, linetype, alpha, etc.).

Examples

Run this code

if (FALSE) {
# Create a stanfit object from reading CSV files of samples (saved in rstan
# package) generated by funtion stan for demonstration purpose from model as follows. 
# 
excode <- '
  transformed data {
    array[20] real y;
    y[1] <- 0.5796;  y[2]  <- 0.2276;   y[3] <- -0.2959; 
    y[4] <- -0.3742; y[5]  <- 0.3885;   y[6] <- -2.1585;
    y[7] <- 0.7111;  y[8]  <- 1.4424;   y[9] <- 2.5430; 
    y[10] <- 0.3746; y[11] <- 0.4773;   y[12] <- 0.1803; 
    y[13] <- 0.5215; y[14] <- -1.6044;  y[15] <- -0.6703; 
    y[16] <- 0.9459; y[17] <- -0.382;   y[18] <- 0.7619;
    y[19] <- 0.1006; y[20] <- -1.7461;
  }
  parameters {
    real mu;
    real sigma;
    vector[2] z[3];
    real alpha;
  } 
  model {
    y ~ normal(mu, sigma);
    for (i in 1:3) 
      z[i] ~ normal(0, 1);
    alpha ~ exponential(2);
  } 
'
# exfit <- stan(model_code = excode, save_dso = FALSE, iter = 200, 
#               sample_file = "rstan_doc_ex.csv")
# 
exfit <- read_stan_csv(dir(system.file('misc', package = 'rstan'),
                       pattern='rstan_doc_ex_[[:digit:]].csv',
                       full.names = TRUE))

print(exfit)
traceplot(exfit)
traceplot(exfit, size = 0.25)
traceplot(exfit, pars = "sigma", inc_warmup = TRUE)

trace <- traceplot(exfit, pars = c("z[1,1]", "z[3,1]"))
trace + scale_color_discrete() + theme(legend.position = "top")
}