MCMC-nuts {bayesplot} | R Documentation |
Diagnostic plots for the No-U-Turn-Sampler (NUTS)
Description
Diagnostic plots for the No-U-Turn-Sampler (NUTS), the default MCMC algorithm used by Stan. See the Plot Descriptions section, below.
Usage
mcmc_nuts_acceptance(
x,
lp,
chain = NULL,
...,
binwidth = NULL,
bins = NULL,
breaks = 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,
bins = NULL,
breaks = NULL,
alpha = 0.5,
merge_chains = FALSE
)
Arguments
x |
A molten data frame of NUTS sampler parameters, either created by
|
lp |
A molten data frame of draws of the log-posterior or, more
commonly, of a quantity equal to the log-posterior up to a constant.
|
chain |
A positive integer for selecting a particular chain. The default
( |
... |
Currently ignored. |
binwidth |
Passed to |
bins |
Passed to |
breaks |
Passed to |
alpha |
For |
merge_chains |
For |
Value
A gtable object (the result of calling
gridExtra::arrangeGrob()
) created from several ggplot objects,
except for mcmc_nuts_energy()
, which returns a ggplot object.
Quick Definitions
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.
Plot Descriptions
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__
vslp__
.
mcmc_nuts_divergence()
-
Two plots:
Violin plots of
lp__|divergent__=1
andlp__|divergent__=0
.Violin plots of
accept_stat__|divergent__=1
andaccept_stat__|divergent__=0
.
mcmc_nuts_stepsize()
-
Two plots:
Violin plots of
lp__
by chain ordered bystepsize__
value.Violin plots of
accept_stat__
by chain ordered bystepsize__
value.
mcmc_nuts_treedepth()
-
Three plots:
Violin plots of
lp__
by value oftreedepth__
.Violin plots of
accept_stat__
by value oftreedepth__
.Histogram of
treedepth__
.
mcmc_nuts_energy()
-
Overlaid histograms showing
energy__
vs the change inenergy__
. See Betancourt (2016) for details.
References
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 No-U-Turn Sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research. 15:1593–1623.
Stan Development Team. Stan Modeling Language Users Guide and Reference Manual. https://mc-stan.org/users/documentation/
See Also
The Visual MCMC Diagnostics vignette.
Several other plotting functions are not NUTS-specific but take optional extra arguments if the model was fit using NUTS:
-
mcmc_trace()
: show divergences as tick marks below the trace plot. -
mcmc_parcoord()
: change the color/size/transparency of lines corresponding to divergences. -
mcmc_scatter()
: change the color/size/shape of points corresponding to divergences. -
mcmc_pairs()
: change the color/size/shape of points corresponding divergences and/or max treedepth saturation.
-
Other MCMC:
MCMC-combos
,
MCMC-diagnostics
,
MCMC-distributions
,
MCMC-intervals
,
MCMC-overview
,
MCMC-parcoord
,
MCMC-recover
,
MCMC-scatterplots
,
MCMC-traces
Examples
## Not run:
library(ggplot2)
library(rstanarm)
fit <- stan_glm(mpg ~ wt + am, data = mtcars, iter = 1000, refresh = 0)
np <- nuts_params(fit)
lp <- log_posterior(fit)
color_scheme_set("brightblue")
mcmc_nuts_acceptance(np, lp)
mcmc_nuts_acceptance(np, lp, chain = 2)
mcmc_nuts_divergence(np, lp)
mcmc_nuts_stepsize(np, lp)
mcmc_nuts_treedepth(np, lp)
color_scheme_set("red")
mcmc_nuts_energy(np)
mcmc_nuts_energy(np, merge_chains = TRUE, binwidth = .15)
mcmc_nuts_energy(np) +
facet_wrap(vars(Chain), nrow = 1) +
coord_fixed(ratio = 150) +
ggtitle("NUTS Energy Diagnostic")
## End(Not run)