Juxtapose prior and posterior

Plot medians and central intervals comparing parameter draws from the prior and posterior distributions. If the plotted priors look different than the priors you think you specified it is likely either because of internal rescaling or the use of the QR argument (see the documentation for the prior_summary method for details on these special cases).

posterior_vs_prior(object, ...)

# S3 method for stanreg posterior_vs_prior( object, pars = NULL, regex_pars = NULL, prob = 0.9, color_by = c("parameter", "vs", "none"), group_by_parameter = FALSE, facet_args = list(), ... )


A fitted model object returned by one of the rstanarm modeling functions. See stanreg-objects.


The S3 generic uses ... to pass arguments to any defined methods. For the method for stanreg objects, ... is for arguments (other than color) passed to geom_pointrange to control the appearance of the plotted intervals.


An optional character vector specifying a subset of parameters to display. Parameters can be specified by name or several shortcuts can be used. Using pars="beta" will restrict the displayed parameters to only the regression coefficients (without the intercept). "alpha" can also be used as a shortcut for "(Intercept)". If the model has varying intercepts and/or slopes they can be selected using pars = "varying".

In addition, for stanmvreg objects there are some additional shortcuts available. Using pars = "long" will display the parameter estimates for the longitudinal submodels only (excluding group-specific pparameters, but including auxiliary parameters). Using pars = "event" will display the parameter estimates for the event submodel only, including any association parameters. Using pars = "assoc" will display only the association parameters. Using pars = "fixef" will display all fixed effects, but not the random effects or the auxiliary parameters. pars and regex_pars are set to NULL then all fixed effect regression coefficients are selected, as well as any auxiliary parameters and the log posterior.

If pars is NULL all parameters are selected for a stanreg object, while for a stanmvreg object all fixed effect regression coefficients are selected as well as any auxiliary parameters and the log posterior. See Examples.


An optional character vector of regular expressions to use for parameter selection. regex_pars can be used in place of pars or in addition to pars. Currently, all functions that accept a regex_pars argument ignore it for models fit using optimization.


A number \(p \in (0,1)\) indicating the desired posterior probability mass to include in the (central posterior) interval estimates displayed in the plot. The default is \(0.9\).


How should the estimates be colored? Use "parameter" to color by parameter name, "vs" to color the prior one color and the posterior another, and "none" to use no color. Except when color_by="none", a variable is mapped to the color aesthetic and it is therefore also possible to change the default colors by adding one of the various discrete color scales available in ggplot2 (scale_color_manual, scale_color_brewer, etc.). See Examples.


Should estimates be grouped together by parameter (TRUE) or by posterior and prior (FALSE, the default)?


A named list of arguments passed to facet_wrap (other than the facets argument), e.g., nrow or ncol to change the layout, scales to allow axis scales to vary across facets, etc. See Examples.


A ggplot object that can be further customized using the ggplot2 package.


Gabry, J. , Simpson, D. , Vehtari, A. , Betancourt, M. and Gelman, A. (2019), Visualization in Bayesian workflow. J. R. Stat. Soc. A, 182: 389-402. doi:10.1111/rssa.12378, (journal version, arXiv preprint, code on GitHub)

  • posterior_vs_prior
  • posterior_vs_prior.stanreg
# }
if (!exists("example_model")) example(example_model)
# display non-varying (i.e. not group-level) coefficients
posterior_vs_prior(example_model, pars = "beta")

# show group-level (varying) parameters and group by parameter
posterior_vs_prior(example_model, pars = "varying",
                   group_by_parameter = TRUE, color_by = "vs")

# group by parameter and allow axis scales to vary across facets
posterior_vs_prior(example_model, regex_pars = "period",
                   group_by_parameter = TRUE, color_by = "none",
                   facet_args = list(scales = "free"))

# assign to object and customize with functions from ggplot2
(gg <- posterior_vs_prior(example_model, pars = c("beta", "varying"), prob = 0.8))

gg + 
 ggplot2::geom_hline(yintercept = 0, size = 0.3, linetype = 3) + 
 ggplot2::coord_flip() + 
 ggplot2::ggtitle("Comparing the prior and posterior")
# compare very wide and very narrow priors using roaches example
# (see help(roaches, "rstanarm") for info on the dataset)
roaches$roach100 <- roaches$roach1 / 100
wide_prior <- normal(0, 10)
narrow_prior <- normal(0, 0.1)
fit_pois_wide_prior <- stan_glm(y ~ treatment + roach100 + senior, 
                                offset = log(exposure2), 
                                family = "poisson", data = roaches, 
                                prior = wide_prior)
posterior_vs_prior(fit_pois_wide_prior, pars = "beta", prob = 0.5, 
                   group_by_parameter = TRUE, color_by = "vs", 
                   facet_args = list(scales = "free"))
fit_pois_narrow_prior <- update(fit_pois_wide_prior, prior = narrow_prior)
posterior_vs_prior(fit_pois_narrow_prior, pars = "beta", prob = 0.5, 
                   group_by_parameter = TRUE, color_by = "vs", 
                   facet_args = list(scales = "free"))

# look at cutpoints for ordinal model
fit_polr <- stan_polr(tobgp ~ agegp, data = esoph, method = "probit",
                      prior = R2(0.2, "mean"), init_r = 0.1)
(gg_polr <- posterior_vs_prior(fit_polr, regex_pars = "\\|", color_by = "vs",
                               group_by_parameter = TRUE))
# flip the x and y axes
gg_polr + ggplot2::coord_flip()
# }
# }
Documentation reproduced from package rstanarm, version 2.19.3, License: GPL (>= 3)

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