DataCamp

Markov chain trace plots

likelihood: Y[i] ~ N(m, s^2)
priors: m ~ N(50, 25^2) and s ~ Unif(0, 200)

A trace plot provides a visualization of a Markov chain's longitudinal behavior. Specifically, a trace plot for the m chain plots the observed chain value (y-axis) against the corresponding iteration number (x-axis).

You will construct trace plots of the m chain using two different approaches: by applying the built-in plot() function to the mcmc.list object sleep_sim and, for finer control over this graphic (and finer control over analyses in later chapters), by applying ggplot() to the data.frame object sleep_chains. Both sleep_sim and sleep_chains are in your workspace:

sleep_sim <- coda.samples(model = sleep_jags, variable.names = c("m", "s"), n.iter = 10000)
sleep_chains <- data.frame(sleep_sim[[1]], iter = 1:10000)

Apply plot() to sleep_sim with density = FALSE to construct trace plots for the m and s chains. NOTE: The 10,000 recorded Iterations start after a "burn-in" period in which samples are discarded. Thus the Iterations count doesn't start at 1!
Apply ggplot() (with a geom_line() layer) to sleep_chains to re-construct the trace plot of the m chain.
Zoom in: construct a ggplot() trace plot of the first 100 iterations of the m chain.

Take Hint (-30 XP)

Exercise

Markov chain trace plots

Instructions