MCMC-diagnostics

0th

Percentile

General MCMC diagnostics

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 No-U-Turn-Sampler, see also MCMC-nuts for additional MCMC diagnostic plots.

Usage
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)

Arguments
rhat

A vector of Rhat estimates.

...

Currently ignored.

size

An optional value to override geom_point's default size (for mcmc_rhat, mcmc_neff) or geom_line's default size (for mcmc_acf).

binwidth

Passed to geom_histogram to override the default binwidth.

breaks

Passed to geom_histogram as an alternative to binwidth.

ratio

A vector of ratios of effective sample size estimates to total sample size. See neff_ratio.

x

A 3-D array, matrix, list of matrices, or data frame of MCMC draws. The MCMC-overview page provides details on how to specify each these allowed inputs.

pars

An optional character vector of parameter names. If neither pars nor regex_pars is specified then the default is to use all parameters.

regex_pars

An optional regular expression to use for parameter selection. Can be specified instead of pars or in addition to pars.

facet_args

A named list of arguments (other than facets) passed to facet_wrap or facet_grid to control faceting.

lags

The number of lags to show in the autocorrelation plot.

Value

A ggplot object that can be further customized using the ggplot2 package. The _data functions return the data that would have been drawn by the plotting function.

Plot Descriptions

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

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.

References

Stan Development Team. (2016). Stan Modeling Language Users Guide and Reference Manual. http://mc-stan.org/documentation/

Gelman, A. and Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science. 7(4), 457--472.

See Also

  • The Visual MCMC Diagnostics vignette.

  • MCMC-nuts for additional MCMC diagnostic plots for models fit using the No-U-Turn-Sampler.

Other MCMC: MCMC-combos, MCMC-distributions, MCMC-intervals, MCMC-nuts, MCMC-overview, MCMC-parcoord, MCMC-recover, MCMC-scatterplots, MCMC-traces

Aliases
  • MCMC-diagnostics
  • mcmc_rhat
  • mcmc_rhat_hist
  • mcmc_rhat_data
  • mcmc_neff
  • mcmc_neff_hist
  • mcmc_neff_data
  • mcmc_acf
  • mcmc_acf_bar
Examples
# NOT RUN {
# autocorrelation
x <- example_mcmc_draws()
dim(x)
dimnames(x)

color_scheme_set("green")
mcmc_acf(x, pars = c("alpha", "beta[1]"))
# }
# NOT RUN {
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")
# }
# NOT RUN {
# 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)
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 y-axis
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)
# }
# NOT RUN {
# }
Documentation reproduced from package bayesplot, version 1.6.0, License: GPL (>= 3)

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