Diagnostic plots for the NoUTurnSampler (NUTS), the default MCMC algorithm used by Stan. See the Plot Descriptions section, below.
mcmc_nuts_acceptance(x, lp, chain = NULL, ..., binwidth = NULL) mcmc_nuts_divergence(x, lp, chain = NULL, ...) mcmc_nuts_stepsize(x, lp, chain = NULL, ...) mcmc_nuts_treedepth(x, lp, chain = NULL, ...) mcmc_nuts_energy(x, ..., binwidth = NULL, alpha = 0.5, merge_chains = FALSE)
x  A molten data frame of NUTS sampler parameters, either created by


lp  A molten data frame of draws of the logposterior or, more
commonly, of a quantity equal to the logposterior up to a constant.

chain  A positive integer for selecting a particular chain. The default
( 
...  Currently ignored. 
binwidth  An optional value used as the 
alpha  For 
merge_chains  For 
A gtable object (the result of calling
arrangeGrob
) created from several ggplot objects,
except for mcmc_nuts_energy
, which returns a ggplot object.
For more details see Stan Development Team (2016) and Betancourt (2017).
accept_stat__
: the average acceptance probabilities of all
possible samples in the proposed tree.
divergent__
: the number of leapfrog transitions with diverging
error. Because NUTS terminates at the first divergence this will be either
0 or 1 for each iteration.
stepsize__
: the step size used by NUTS in its Hamiltonian
simulation.
treedepth__
: the depth of tree used by NUTS, which is the log
(base 2) of the number of leapfrog steps taken during the Hamiltonian
simulation.
energy__
: the value of the Hamiltonian (up to an additive
constant) at each iteration.
mcmc_nuts_acceptance
Three plots:
Histogram of accept_stat__
with vertical lines indicating the
mean (solid line) and median (dashed line).
Histogram of lp__
with vertical
lines indicating the mean (solid line) and median (dashed line).
Scatterplot of accept_stat__
vs lp__
.
mcmc_nuts_divergence
Two plots:
Violin plots of lp__divergent__=1
and
lp__divergent__=0
.
Violin plots of accept_stat__divergent__=1
and
accept_stat__divergent__=0
.
mcmc_nuts_stepsize
Two plots:
Violin plots of lp__
by chain ordered by
stepsize__
value.
Violin plots of accept_stat__
by chain ordered by
stepsize__
value.
mcmc_nuts_treedepth
Three plots:
Violin plots of lp__
by value of treedepth__
.
Violin plots of accept_stat__
by value of treedepth__
.
Histogram of treedepth__
.
mcmc_nuts_energy
Overlaid histograms showing energy__
vs the change in
energy__
. See Betancourt (2016) for details.
Betancourt, M. (2017). A conceptual introduction to Hamiltonian Monte Carlo. https://arxiv.org/abs/1701.02434
Betancourt, M. and Girolami, M. (2013). Hamiltonian Monte Carlo for hierarchical models. https://arxiv.org/abs/1312.0906
Hoffman, M. D. and Gelman, A. (2014). The NoUTurn Sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research. 15:15931623.
Stan Development Team. (2016). Stan Modeling Language Users Guide and Reference Manual. http://mcstan.org/documentation/
The Visual MCMC Diagnostics vignette.
Several other plotting functions in the bayesplot package that are not NUTSspecific but take optional extra arguments if the model was fit using NUTS:
mcmc_trace
will plot divergences on the traceplot if the
optional divergences
argument is specified.
mcmc_pairs
will indicate which (if any) iterations
encountered a divergent transition or hit the maximum treedepth (rather than
terminated its evolution normally).
Other MCMC: MCMCcombos
,
MCMCdiagnostics
,
MCMCdistributions
,
MCMCintervals
,
MCMCoverview
, MCMCrecover
,
MCMCscatterplots
,
MCMCtraces
# NOT RUN { library(ggplot2) library(rstanarm) fit < stan_glm(mpg ~ wt + am, data = mtcars, iter = 1000) np < nuts_params(fit) lp < log_posterior(fit) color_scheme_set("brightblue") mcmc_nuts_acceptance(np, lp) mcmc_nuts_acceptance(np, lp, chain = 2) color_scheme_set("red") mcmc_nuts_energy(np) mcmc_nuts_energy(np, merge_chains = TRUE, binwidth = .15) mcmc_nuts_energy(np) + facet_wrap(~ Chain, nrow = 1) + coord_fixed(ratio = 150) + ggtitle("NUTS Energy Diagnostic") # }