The implementation of ggmcmc follows this scheme and is based on a function ( ggs()) that transforms the original input (time series of sampled values for different parameters and chains) into a data frame that is used for all the graphing functions. The flexibility comes from the fact that it is very easy to extend basic graphics by including more aesthetic elements and geometric objects, and even faceting the figure to generate the same plot for different subsets of the dataset (Wickham 2009, 3). Therefore, in order to create a graphic the three components must be supplied: a data frame, at least one aesthetic attribute and at least one geometric object. Ggplot2 is based on the idea that the input of any graphic is a data frame mapped to aesthetic attributes (colour, size) of geometric objects (points, lines). Based on this idea, ggmcmc is aimed at bringing the design and implementation of ggplot2 to MCMC diagnostics, allowing Bayesian inference users to have better and more flexible visual diagnostic tools. 2005), empowers R users by allowing them to flexibly crate graphics (Wickham 2009). Ggplot2, based on the grammar of graphics (Wilkinson et al.
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