# Visualizing and wrangling MCMC output in R with `MCMCvis`

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Model results can be thought of as a reward for the many hours of model design, troubleshooting, re-design, etc. that analyses often require. Following the potentially exhausting mental exercise to acquire these results, I think we’d all like the interpretation to be as straightforward as possible. Analyzing MCMC output from Bayesian analyses, which may include hundreds of parameters and/or derived quantities, however can often require a fair amount of code and (more importantly) time.

The `MCMCvis`

package was designed to alleviate this problem, and streamline the analysis of Bayesian model results. The latest version (0.7.1) is now available on CRAN with bug fixes, speed improvements, and added functionality.

## Why `MCMCvis`

?

Using `MCMCvis`

provides three principal benefits:

1) MCMC output fit with Stan, JAGS, or other MCMC samplers can be fed into all package functions as an argument with no further manipulation needed. No need to specify the type of object or how it was fit; the package does all of that behind the scenes.

2) Specific parameters or derived quantities of interest can be specified within each function, to avoid additional steps of data processing. This works using a `grep`

like call for optimal efficiency.

3) The package creates ‘publication-ready’ posterior estimate visualizations (below). Parameters can now be plotted vertically or horizontally.

## The package has four functions for basic MCMC output tasks:

`MCMCsummary`

– summarize MCMC output for particular parameters of interest

`MCMCtrace`

– create trace and density plots of MCMC chains for particular parameters of interest

`MCMCchains`

– easily extract posterior chains from MCMC output for particular parameters of interest

`MCMCplot`

– create caterpillar plots from MCMC output for particular parameters of interest

The vignette can be found here.

## An example workflow may go as follows:

**– summarize posterior estimates for just beta parameters**

#install package install.packages('MCMCvis') #load package require(MCMCvis) #load example data data(MCMC_data) #run summary function MCMCsummary(MCMC_data, params = 'beta') ## mean 2.5% 50% 97.5% Rhat ## beta[1] 0.16 0.06 0.15 0.25 1 ## beta[2] -7.77 -25.82 -7.68 9.78 1 ## beta[3] -5.64 -28.53 -5.76 17.23 1 ## beta[4] -10.39 -25.98 -10.63 5.27 1 ## beta[5] 7.52 6.03 7.52 9.05 1 ## beta[6] 10.89 10.10 10.89 11.68 1 ## beta[7] -1.91 -4.83 -1.92 1.08 1 ## beta[8] 5.38 -6.86 5.45 17.67 1 ## beta[9] 13.39 3.28 13.38 23.60 1 ## beta[10] 17.63 14.41 17.63 20.86 1

**– check posteriors for convergence**

MCMCtrace(MCMC_data, params = c('beta[1]', 'beta[2]', 'beta[3]'), ind = TRUE)

**– extract chains for beta parameters so that they can be manipulated directly**

ex <- MCMCchains(MCMC_data, params = 'beta') #find 22nd quantile for all beta parameters apply(ex, 2, function(x){round(quantile(x, probs = 0.22), digits = 2)}) ## beta[1] beta[2] beta[3] beta[4] beta[5] beta[6] beta[7] beta[8] beta[9] beta[10] ## 0.12 -14.86 -14.80 -16.48 6.91 10.58 -3.09 0.68 9.29 16.36

**– create caterpillar plots for posterior estimates. Shading represents whether 50% CI (gray with open circle), 95% CI (gray with closed circle), or neither (black) overlap 0. This option can be turned off (as below). A variety of options exist, including the ability to plot posteriors vertically rather than horizontally**

MCMCplot(MCMC_data, params = 'beta', horiz = FALSE, rank = TRUE, ref_ovl = FALSE, xlab = 'My x-axis label', main = 'MCMCvis plot', labels = c('First param', 'Second param', 'Third param', 'Fourth param', 'Fifth param', 'Sixth param', 'Seventh param', 'Eighth param', 'Nineth param', 'Tenth param'), labels_sz = 1.5, med_sz = 2, thick_sz = 7, thin_sz = 3, ax_sz = 4, main_text_sz = 2)

## Follow Casey Youngflesh on Twitter @caseyyoungflesh. The `MCMCvis`

source code can be found on GitHub.

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