RStan Diagnostic plots
Diagnostic plots for HMC and NUTS using ggplot2.
stan_diag(object,
information = c("sample","stepsize", "treedepth","divergence"),
chain = 0, ...)
stan_par(object, par, chain = 0, ...)
stan_rhat(object, pars, ...)
stan_ess(object, pars, ...)
stan_mcse(object, pars, ...)object |
A stanfit or stanreg object. |
information |
The information to be contained in the diagnostic plot. |
par,pars |
The name of a single scalar parameter ( |
chain |
If |
... |
For |
stan_rhat,stan_ess,stan_mcse
Respectively, these plots show the distribution of the Rhat statistic, the ratio of effective sample size to total sample size, and the ratio of Monte Carlo standard error to posterior standard deviation for the estimated parameters. These plots are not intended to identify individual parameters, but rather to allow for quickly identifying if the estimated values of these quantities are desireable for all parameters.
stan_parCalling stan_par generates three plots:
(i) a scatterplot of par vs. the accumulated log-posterior (lp__),
(ii) a scatterplot of par vs. the average Metropolis acceptance rate
(accept_stat), and
(iii) a violin plot showing the distribution of par at each of the
sampled step sizes (one per chain).
For the scatterplots, red points are superimposed to indicate which
(if any) iterations encountered a divergent transition. Yellow points indicate
a transition that hit the maximum treedepth rather than terminated its
evolution normally.
stan_diagThe information argument is used to specify which
plots stan_diag should generate:
information='sample'Histograms of lp__
and accept_stat, as well as a scatterplot showing their
joint distribution.
information='stepsize'Violin plots showing the
distributions of lp__ and accept_stat at each of the sampled
step sizes (one per chain).
information='treedepth'Histogram of treedepth and
violin plots showing the distributions of lp__ and
accept_stat for each value of treedepth.
information='divergence'Violin plots showing the
distributions of lp__ and accept_stat for iterations that
encountered divergent transitions (divergent=1) and those that
did not (divergent=0).
For stan_diag and stan_par, a list containing the ggplot objects for
each of the displayed plots. For stan_rhat, stan_ess,
and stan_mcse, a single ggplot object.
For details about the individual diagnostics and sampler parameters and their interpretations see the Stan Modeling Language User's Guide and Reference Manual at http://mc-stan.org/documentation/.
## Not run:
fit <- stan_demo("eight_schools")
stan_diag(fit, info = 'sample') # shows three plots together
samp_info <- stan_diag(fit, info = 'sample') # saves the three plots in a list
samp_info[[3]] # access just the third plot
stan_diag(fit, info = 'sample', chain = 1) # overlay chain 1
stan_par(fit, par = "mu")
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