Coda 2 tidy html
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The goal is for tidybayes to do the tedious work of figuring out how to make a data frame look the way you need it to, including turning parameters with indices like "b" and the like into tidy data for you.įit into the tidyverse. A similar function to ggmcmc’s approach is also provided in gather_draws, since sometimes you do want variable names as values in a column. In contrast to the ggmcmc library (which translates model results into a data frame with a Parameter and value column), the spread_draws function in tidybayes produces data frames where the columns are named after parameters and (in some cases) indices of those parameters, as automatically as possible and using a syntax as close to the same way you would refer to those variables in the model’s language as possible. Tidy data does not always mean all parameter names as values. There are a few core ideas that run through the tidybayes API that should (hopefully) make it easy to use: tidybayes automates all of these sorts of tasks. Output formats will often be in matrix form (requiring conversion for use with libraries like ggplot), and will use numeric indices (requiring conversion back into factor level names if the you wish to make meaningfully-labeled plots or tables). For example, input formats might expect a list instead of a data frame, and for all variables to be encoded as numeric values (requiring translation of factors to numeric values and the creation of index variables to store the number of levels per factor or the number of observations in a data frame). The default output (and sometimes input) data formats of popular modeling functions like JAGS and Stan often don’t quite conform to the ideal of tidy data. This vignette also describes how to use ggdist (the sister package to tidybayes) for visualizing model output.
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For a similar introduction to the use of tidybayes with high-level modeling functions such as those in brms or rstanarm, see vignette("tidy-brms") or vignette("tidy-rstanarm"). This vignette is geared towards working with tidy data in general-purpose modeling functions like JAGS or Stan. This vignette introduces the tidybayes package, which facilitates the use of tidy data (one observation per row) with Bayesian models in R.