R: Plot interval estimates from MCMC draws

MCMC-intervals {bayesplot}

R Documentation

Plot interval estimates from MCMC draws

Description

Plot central (quantile-based) posterior interval estimates from MCMC draws. See the Plot Descriptions section, below, for details.

Usage

mcmc_intervals(x, pars = character(), regex_pars = character(),
  transformations = list(), ..., prob = 0.5, prob_outer = 0.9,
  point_est = c("median", "mean", "none"), rhat = numeric())

mcmc_areas(x, pars = character(), regex_pars = character(),
  transformations = list(), ..., prob = 0.5, prob_outer = 1,
  point_est = c("median", "mean", "none"), rhat = numeric(), bw = NULL,
  adjust = NULL, kernel = NULL, n_dens = NULL)

mcmc_areas_ridges(x, pars = character(), regex_pars = character(),
  transformations = list(), ..., prob_outer = 1, prob = 1, bw = NULL,
  adjust = NULL, kernel = NULL, n_dens = NULL)

mcmc_intervals_data(x, pars = character(), regex_pars = character(),
  transformations = list(), ..., prob = 0.5, prob_outer = 0.9,
  point_est = c("median", "mean", "none"), rhat = numeric())

mcmc_areas_data(x, pars = character(), regex_pars = character(),
  transformations = list(), ..., prob = 0.5, prob_outer = 1,
  point_est = c("median", "mean", "none"), rhat = numeric(), bw = NULL,
  adjust = NULL, kernel = NULL, n_dens = NULL)

mcmc_areas_ridges_data(x, pars = character(), regex_pars = character(),
  transformations = list(), ..., prob_outer = 1, prob = 1, bw = NULL,
  adjust = NULL, kernel = NULL, n_dens = NULL)

Arguments

`x`	A 3-D array, matrix, list of matrices, or data frame of MCMC draws. The MCMC-overview page provides details on how to specify each these allowed inputs.
`pars`	An optional character vector of parameter names. If neither `pars` nor `regex_pars` is specified then the default is to use all parameters.
`regex_pars`	An optional regular expression to use for parameter selection. Can be specified instead of `pars` or in addition to `pars`.
`transformations`	Optionally, transformations to apply to parameters before plotting. If `transformations` is a function or a single string naming a function then that function will be used to transform all parameters. To apply transformations to particular parameters, the `transformations` argument can be a named list with length equal to the number of parameters to be transformed. Currently only univariate transformations of scalar parameters can be specified (multivariate transformations will be implemented in a future release). If `transformations` is a list, the name of each list element should be a parameter name and the content of each list element should be a function (or any item to match as a function via `match.fun`, e.g. a string naming a function). If a function is specified by its name as a string (e.g. `"log"`), then it can be used to construct a new parameter label for the appropriate parameter (e.g. `"log(sigma)"`). If a function itself is specified (e.g. `log` or `function(x) log(x)`) then `"t"` is used in the new parameter label to indicate that the parameter is transformed (e.g. `"t(sigma)"`).
`...`	Currently unused.
`prob`	The probability mass to include in the inner interval (for `mcmc_intervals`) or in the shaded region (for `mcmc_areas`). The default is `0.5` (50% interval) and `1` for `mcmc_areas_ridges`.
`prob_outer`	The probability mass to include in the outer interval. The default is `0.9` for `mcmc_intervals` (90% interval) and `1` for `mcmc_areas` and for `mcmc_areas_ridges`.
`point_est`	The point estimate to show. Either `"median"` (the default), `"mean"`, or `"none"`.
`rhat`	An optional numeric vector of Rhat estimates, with one element per parameter included in `x`. If `rhat` is provided, the intervals/areas and point estimates in the resulting plot are colored based on Rhat value. See `rhat` for methods for extracting Rhat estimates.
`bw, adjust, kernel, n_dens`	Optional arguments passed to `density` to override default kernel density estimation parameters. `n_dens` defaults to 1024.

Value

A ggplot object that can be further customized using the ggplot2 package. The _data functions return the data that would have be drawn by the plotting function.

Plot Descriptions

mcmc_intervals: Plots of uncertainty intervals computed from posterior draws with all chains merged.
mcmc_areas: Density plots computed from posterior draws with all chains merged, with uncertainty intervals shown as shaded areas under the curves.
mcmc_areas_ridges: Density plot, as in mcmc_areas, but drawn with overlapping ridgelines. This plot provides a compact display of (hierarchically) related distributions.

Examples

set.seed(9262017)
# some parameter draws to use for demonstration
x <- example_mcmc_draws(params = 6)
dim(x)
dimnames(x)

color_scheme_set("brightblue")
mcmc_intervals(x)
mcmc_intervals(x, pars = c("beta[1]", "beta[2]"))
mcmc_areas(x, regex_pars = "beta\\[[1-3]", prob = 0.8) +
 ggplot2::labs(
   title = "Posterior distributions",
   subtitle = "with medians and 80% intervals"
 )

color_scheme_set("red")
mcmc_areas(
   x,
   pars = c("alpha", "beta[4]"),
   prob = 2/3,
   prob_outer = 0.9,
   point_est = "mean"
)

# color by rhat value
color_scheme_set("blue")
fake_rhat_values <- c(1, 1.07, 1.3, 1.01, 1.15, 1.005)
mcmc_intervals(x, rhat = fake_rhat_values)

mcmc_intervals_data(x)
mcmc_intervals_data(x, rhat = fake_rhat_values)
mcmc_areas_data(x, pars = "alpha")

color_scheme_set("gray")
p <- mcmc_areas(x, pars = c("alpha", "beta[4]"), rhat = c(1, 1.1))
p + legend_move("bottom")
p + legend_move("none") # or p + legend_none()


# apply transformations
mcmc_intervals(
  x,
  pars = c("beta[2]", "sigma"),
  transformations = list("sigma" = "log", "beta[2]" = function(x) x + 3)
)

# apply same transformation to all selected parameters
mcmc_intervals(x, regex_pars = "beta", transformations = "exp")


## Not run: 
# example using fitted model from rstanarm package
library(rstanarm)
fit <- stan_glm(
 mpg ~ 0 + wt + factor(cyl),
 data = mtcars,
 iter = 500
)
x <- as.matrix(fit)

color_scheme_set("teal")
mcmc_intervals(x, point_est = "mean", prob = 0.8, prob_outer = 0.95)
mcmc_areas(x, regex_pars = "cyl", bw = "SJ",
           rhat = rhat(fit, regex_pars = "cyl"))

## End(Not run)

## Not run: 
# Example of hierarchically related parameters
# plotted with ridgelines
m <- shinystan::eight_schools@posterior_sample
mcmc_areas_ridges(m, pars = "mu", regex_pars = "theta") +
 ggplot2::ggtitle("Treatment effect on eight schools (Rubin, 1981)")

## End(Not run)

[Package bayesplot version 1.5.0 Index]