|Diagnostic plots||R Documentation|
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, ...)
A stanfit or stanreg object.
The information to be contained in the diagnostic plot.
The name of a single scalar parameter (
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_par generates three plots:
(i) a scatterplot of
par vs. the accumulated log-posterior (
(ii) a scatterplot of
par vs. the average Metropolis acceptance rate
(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
information argument is used to specify which
stan_diag should generate:
accept_stat, as well as a scatterplot showing their
information='stepsize'Violin plots showing the
accept_stat at each of the sampled
step sizes (one per chain).
violin plots showing the distributions of
accept_stat for each value of
information='divergence'Violin plots showing the
accept_stat for iterations that
encountered divergent transitions (
divergent=1) and those that
did not (
stan_par, a list containing the ggplot objects for
each of the displayed plots. For
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 https://mc-stan.org/documentation/.
List of RStan plotting functions,
## 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[] # access just the third plot stan_diag(fit, info = 'sample', chain = 1) # overlay chain 1 stan_par(fit, par = "mu") ## End(Not run)
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