MCMCdiagnostics {bayesplot}  R Documentation 
Plots of Rhat statistics, ratios of effective sample size to total sample size, and autocorrelation of MCMC draws. See the Plot Descriptions section, below, for details. For models fit using the NoUTurnSampler, see also MCMCnuts for additional MCMC diagnostic plots.
mcmc_rhat(rhat, ..., size = NULL) mcmc_rhat_hist(rhat, ..., binwidth = NULL, breaks = NULL) mcmc_rhat_data(rhat, ...) mcmc_neff(ratio, ..., size = NULL) mcmc_neff_hist(ratio, ..., binwidth = NULL, breaks = NULL) mcmc_neff_data(ratio, ...) mcmc_acf( x, pars = character(), regex_pars = character(), ..., facet_args = list(), lags = 20, size = NULL ) mcmc_acf_bar( x, pars = character(), regex_pars = character(), ..., facet_args = list(), lags = 20 )
rhat 
A vector of Rhat estimates. 
... 
Currently ignored. 
size 
An optional value to override 
binwidth 
Passed to 
breaks 
Passed to 
ratio 
A vector of ratios of effective sample size estimates to
total sample size. See 
x 
A 3D array, matrix, list of matrices, or data frame of MCMC draws.
The MCMCoverview page provides details on how to specify each these
allowed inputs. It is also possible to use an object with an

pars 
An optional character vector of parameter names. If neither

regex_pars 
An optional regular expression to use for
parameter selection. Can be specified instead of 
facet_args 
A named list of arguments (other than 
lags 
The number of lags to show in the autocorrelation plot. 
The plotting functions return a ggplot object that can be further
customized using the ggplot2 package. The functions with suffix
_data()
return the data that would have been drawn by the plotting
function.
mcmc_rhat()
, mcmc_rhat_hist()
Rhat values as either points or a histogram. Values are colored using different shades (lighter is better). The chosen thresholds are somewhat arbitrary, but can be useful guidelines in practice.
light: below 1.05 (good)
mid: between 1.05 and 1.1 (ok)
dark: above 1.1 (too high)
mcmc_neff()
, mcmc_neff_hist()
Ratios of effective sample size to total sample size as either points or a histogram. Values are colored using different shades (lighter is better). The chosen thresholds are somewhat arbitrary, but can be useful guidelines in practice.
light: between 0.5 and 1 (high)
mid: between 0.1 and 0.5 (good)
dark: below 0.1 (low)
mcmc_acf()
, mcmc_acf_bar()
Grid of autocorrelation plots by chain and parameter. The lags
argument
gives the maximum number of lags at which to calculate the autocorrelation
function. mcmc_acf()
is a line plot whereas mcmc_acf_bar()
is a
barplot.
Stan Development Team. Stan Modeling Language Users Guide and Reference Manual. https://mcstan.org/users/documentation/
Gelman, A. and Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science. 7(4), 457–472.
The Visual MCMC Diagnostics vignette.
MCMCnuts for additional MCMC diagnostic plots for models fit using the NoUTurnSampler.
Other MCMC:
MCMCcombos
,
MCMCdistributions
,
MCMCintervals
,
MCMCnuts
,
MCMCoverview
,
MCMCparcoord
,
MCMCrecover
,
MCMCscatterplots
,
MCMCtraces
# autocorrelation x < example_mcmc_draws() dim(x) dimnames(x) color_scheme_set("green") mcmc_acf(x, pars = c("alpha", "beta[1]")) color_scheme_set("pink") (p < mcmc_acf_bar(x, pars = c("alpha", "beta[1]"))) # add horiztonal dashed line at 0.5 p + hline_at(0.5, linetype = 2, size = 0.15, color = "gray") # fake rhat values to use for demonstration rhat < c(runif(100, 1, 1.15)) mcmc_rhat_hist(rhat) mcmc_rhat(rhat) # lollipops color_scheme_set("purple") mcmc_rhat(rhat[1:10], size = 5) color_scheme_set("blue") mcmc_rhat(runif(1000, 1, 1.07)) mcmc_rhat(runif(1000, 1, 1.3)) + legend_move("top") # add legend above plot # fake neff ratio values to use for demonstration ratio < c(runif(100, 0, 1)) mcmc_neff_hist(ratio) mcmc_neff(ratio) ## Not run: # Example using rstanarm model (requires rstanarm package) library(rstanarm) # intentionally use small 'iter' so there are some # problems with rhat and neff for demonstration fit < stan_glm(mpg ~ ., data = mtcars, iter = 50, refresh = 0) rhats < rhat(fit) ratios < neff_ratio(fit) mcmc_rhat(rhats) mcmc_neff(ratios, size = 3) # there's a small enough number of parameters in the # model that we can display their names on the yaxis mcmc_neff(ratios) + yaxis_text(hjust = 1) # can also look at autocorrelation draws < as.array(fit) mcmc_acf(draws, pars = c("wt", "cyl"), lags = 10) # increase number of iterations and plots look much better fit2 < update(fit, iter = 500) mcmc_rhat(rhat(fit2)) mcmc_neff(neff_ratio(fit2)) mcmc_acf(as.array(fit2), pars = c("wt", "cyl"), lags = 10) ## End(Not run)