R: General MCMC diagnostics

MCMC-diagnostics {bayesplot}

R Documentation

General MCMC diagnostics

Description

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, bins = NULL, breaks = NULL)

mcmc_rhat_data(rhat, ...)

mcmc_neff(ratio, ..., size = NULL)

mcmc_neff_hist(ratio, ..., binwidth = NULL, bins = 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 R-hat estimates.
`...`	Currently ignored.
`size`	Optional values to override `ggplot2::geom_point()`'s default size (for `mcmc_rhat()`, `mcmc_neff()`) or `ggplot2::geom_line()`'s default line width (for `mcmc_acf()`).
`binwidth`	Passed to `ggplot2::geom_histogram()` to override the default binwidth.
`bins`	Passed to `ggplot2::geom_histogram()` to override the default binwidth.
`breaks`	Passed to `ggplot2::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`	An object containing MCMC draws: A 3-D array, matrix, list of matrices, or data frame. The MCMC-overview page provides details on how to specify each these. A `draws` object from the posterior package (e.g., `draws_array`, `draws_rvars`, etc.). An object with an `as.array()` method that returns the same kind of 3-D array described on the MCMC-overview page.
`pars`	An optional character vector of parameter names. If neither `pars` nor `regex_pars` is specified then the default is to use all parameters. As of version `⁠1.7.0⁠`, bayesplot also supports 'tidy' parameter selection by specifying `pars = vars(...)`, where `...` is specified the same way as in dplyr::select(...) and similar functions. Examples of using `pars` in this way can be found on the Tidy parameter selection page.
`regex_pars`	An optional regular expression to use for parameter selection. Can be specified instead of `pars` or in addition to `pars`. When using `pars` for tidy parameter selection, the `regex_pars` argument is ignored since select helpers perform a similar function.
`facet_args`	A named list of arguments (other than `facets`) passed to `ggplot2::facet_wrap()` or `ggplot2::facet_grid()` to control faceting. Note: if `scales` is not included in `facet_args` then bayesplot may use `scales="free"` as the default (depending on the plot) instead of the ggplot2 default of `scales="fixed"`.
`lags`	The number of lags to show in the autocorrelation plot.

Value

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.

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(), 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.

References

Stan Development Team. Stan Modeling Language Users Guide and Reference Manual. https://mc-stan.org/users/documentation/

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

Examples

# 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 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)

## End(Not run)

[Package bayesplot version 1.11.1 Index]