ggs_grb: Gelman-Rubin-Brooks plot (Rhat shrinkage)

In ggmcmc: Tools for Analyzing MCMC Simulations from Bayesian Inference

Description

Generate a Figure with the Rhat shrinkage evolution over bins of simulations, known as the Gelman-Rubin-Brooks plot, or the Gelman plot. For the Potential Scale Reduction Factor (Rhat), proposed by Gelman and Rubin (1992), the version from the second edition of Bayesian Data Analysis (Gelman, Carlin, Stern and Rubin) is used, but the version used in the package "coda" can also be used (Brooks & Gelman 1998).

Usage

ggs_grb(
  D,
  family = NA,
  scaling = 1.5,
  greek = FALSE,
  version_rhat = "BDA2",
  bins = 50,
  plot = TRUE
)

Arguments

D	Data frame whith the simulations
family	Name of the family of parameters to plot, as given by a character vector or a regular expression. A family of parameters is considered to be any group of parameters with the same name but different numerical value between square brackets (as beta[1], beta[2], etc).
scaling	Value of the upper limit for the x-axis. By default, it is 1.5, to help contextualization of the convergence. When 0 or NA, the axis are not scaled.
greek	Logical value indicating whether parameter labels have to be parsed to get Greek letters. Defaults to false.
version_rhat	Character variable with the name of the version of the potential scale reduction factor to use. Defaults to "BDA2", which refers to the second version of _Bayesian Data Analysis_ (Gelman, Carlin, Stern and Rubin). The other available version is "BG98", which refers to Brooks & Gelman (1998) and is the one used in the "coda" package.
bins	Numerical value with the number of bins requested. Defaults to 50.
plot	Logical value indicating whether the plot must be returned (the default) or a tidy dataframe with the results of the Rhat diagnostics per Parameter.

Details

Notice that at least two chains are required.

Value

A ggplot object, or a tidy data frame.

References

Fernández-i-Marín, Xavier (2016) ggmcmc: Analysis of MCMC Samples and Bayesian Inference. Journal of Statistical Software, 70(9), 1-20. doi:10.18637/jss.v070.i09

Gelman, Carlin, Stern and Rubin (2003) Bayesian Data Analysis. 2nd edition. Chapman & Hall/CRC, Boca Raton.

Gelman, A and Rubin, DB (1992) Inference from iterative simulation using multiple sequences, _Statistical Science_, *7*, 457-511.

Brooks, S. P., and Gelman, A. (1998). General methods for monitoring convergence of iterative simulations. _Journal of computational and graphical statistics_, 7(4), 434-455.

Examples

data(linear)
ggs_grb(ggs(s))

Example output

Loading required package: dplyr

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union

Loading required package: tidyr
Loading required package: ggplot2
Registered S3 method overwritten by 'GGally':
  method from   
  +.gg   ggplot2

ggmcmc documentation built on Feb. 10, 2021, 5:10 p.m.