Convenience functions for adding to (and changing details of) ggplot objects (many of the objects returned by bayesplot functions). See the Examples section, below.

vline_at(v, fun, ..., na.rm = TRUE)

hline_at(v, fun, ..., na.rm = TRUE)

vline_0(..., na.rm = TRUE)

hline_0(..., na.rm = TRUE)

abline_01(..., na.rm = TRUE)

lbub(p, med = TRUE)

legend_move(position = "right")

legend_none()

legend_text(...)

xaxis_title(on = TRUE, ...)

xaxis_text(on = TRUE, ...)

xaxis_ticks(on = TRUE, ...)

yaxis_title(on = TRUE, ...)

yaxis_text(on = TRUE, ...)

yaxis_ticks(on = TRUE, ...)

facet_text(on = TRUE, ...)

facet_bg(on = TRUE, ...)

panel_bg(on = TRUE, ...)

plot_bg(on = TRUE, ...)

grid_lines(color = "gray50", size = 0.2)

overlay_function(...)

Arguments

v

Either a numeric vector specifying the value(s) at which to draw the vertical or horizontal line(s), or an object of any type to use as the first argument to fun.

fun

A function, or the name of a function, that returns a numeric vector.

...

For the various vline_, hline_, and abline_ functions, ... is passed to geom_vline, geom_hline, and geom_abline, respectively, to control the appearance of the line(s).

For functions ending in _bg, ... is passed to element_rect.

For functions ending in _text or _title, ... is passed to element_text.

For xaxis_ticks and yaxis_ticks, ... is passed to element_line.

For overlay_function, ... is passed to stat_function.

na.rm

A logical scalar passed to the appropriate geom (e.g. geom_vline). The default is TRUE.

p

The probability mass (in [0,1]) to include in the interval.

med

Should the median also be included in addition to the lower and upper bounds of the interval?

position

The position of the legend. Either a numeric vector (of length 2) giving the relative coordinates (between 0 and 1) for the legend, or a string among "right", "left", "top", "bottom". Using position = "none" is also allowed and is equivalent to using legend_none().

on

For functions modifying ggplot theme elements, set on=FALSE to set the element to element_blank. For example, facet text can be removed by adding facet_text(on=FALSE), or simply facet_text(FALSE) to a ggplot object. If on=TRUE (the default), then ... can be used to customize the appearance of the theme element.

color, size

Passed to element_line.

Value

A ggplot2 layer or theme object that can be added to existing ggplot objects, like those created by many of the bayesplot plotting functions. See the Details section.

Details

Add vertical, horizontal, and diagonal lines to plots

  • vline_at and hline_at return an object created by either geom_vline or geom_hline that can be added to a ggplot object to draw a vertical or horizontal line (at one or several values). If fun is missing then the lines are drawn at the values in v. If fun is specified then the lines are drawn at the values returned by fun(v).

  • vline_0 and hline_0 are wrappers for vline_at and hline_at with v = 0 and fun missing.

  • abline_01 is a wrapper for geom_abline with the intercept set to 0 and the slope set to 1.

  • lbub returns a function that takes a single argument x and returns the lower and upper bounds (lb, ub) of the 100*p% central interval of x, as well as the median (if med is TRUE).

Control appearance of facet strips

  • facet_text and facet_bg return ggplot2 theme objects that can be added to an existing plot (ggplot object) to format the text and the background for the facet strips.

Move legend, remove legend, or style the legend text

  • legend_move and legend_none return a ggplot2 theme object that can be added to an existing plot (ggplot object) in order to change the position of the legend (legend_move) or remove the legend (legend_none). legend_text works much like facet_text, except it controls the legend text.

Control appearance of \(x\)-axis and \(y\)-axis features

  • xaxis_title and yaxis_title return a ggplot2 theme object that can be added to an existing plot (ggplot object) in order to toggle or format the titles displayed on the x or y axis. (To change the titles themselves use labs.)

  • xaxis_text and yaxis_text return a ggplot2 theme object that can be added to an existing plot (ggplot object) in order to toggle or format the text displayed on the x or y axis (e.g. tick labels).

  • xaxis_ticks and yaxis_ticks return a ggplot2 theme object that can be added to an existing plot (ggplot object) to change the appearance of the axis tick marks.

Customize plot background

  • plot_bg returns a ggplot2 theme object that can be added to an existing plot (ggplot object) to format the background of the entire plot.

  • panel_bg returns a ggplot2 theme object that can be added to an existing plot (ggplot object) to format the background of the just the plotting area.

  • grid_lines returns a ggplot2 theme object that can be added to an existing plot (ggplot object) to add grid lines to the plot background.

Superimpose a function on an existing plot

  • overlay_function is a simple wrapper for stat_function but with the inherit.aes argument fixed to FALSE. Fixing inherit.aes=FALSE will avoid potential errors due to the aesthetic mapping used by certain bayesplot plotting functions).

See also

theme_default for the default ggplot theme used by bayesplot.

Examples

color_scheme_set("gray") x <- example_mcmc_draws(chains = 1) dim(x)
#> [1] 250 4
colnames(x)
#> [1] "alpha" "sigma" "beta[1]" "beta[2]"
################################### ### vertical & horizontal lines ### ################################### (p <- mcmc_intervals(x, regex_pars = "beta"))
# vertical line at zero (with some optional styling) p + vline_0()
p + vline_0(size = 0.25, color = "darkgray", linetype = 2)
# vertical line(s) at specified values v <- c(-0.5, 0, 0.5) p + vline_at(v, linetype = 3, size = 0.25)
my_lines <- vline_at(v, alpha = 0.25, size = 0.75 * c(1, 2, 1), color = c("maroon", "skyblue", "violet")) p + my_lines
# add vertical line(s) at computed values # (three ways of getting lines at column means) color_scheme_set("brightblue") p <- mcmc_intervals(x, regex_pars = "beta") p + vline_at(x[, 3:4], colMeans)
p + vline_at(x[, 3:4], "colMeans", color = "darkgray", lty = 2, size = 0.25)
p + vline_at(x[, 3:4], function(a) apply(a, 2, mean), color = "orange", size = 2, alpha = 0.1)
# using the lbub function to get interval lower and upper bounds (lb, ub) color_scheme_set("pink") parsed <- ggplot2::label_parsed p2 <- mcmc_hist(x, pars = "beta[1]", binwidth = 1/20, facet_args = list(labeller = parsed)) (p2 <- p2 + facet_text(size = 16))
b1 <- x[, "beta[1]"] p2 + vline_at(b1, fun = lbub(0.8), color = "gray20", size = 2 * c(1,.5,1), alpha = 0.75)
p2 + vline_at(b1, lbub(0.8, med = FALSE), color = "gray20", size = 2, alpha = 0.75)
########################## ### format axis titles ### ########################## color_scheme_set("green") y <- example_y_data() yrep <- example_yrep_draws() (p3 <- ppc_stat(y, yrep, stat = "median", binwidth = 1/4))
# turn off the legend, turn on x-axis title p3 + legend_none() + xaxis_title(size = 13, family = "sans") + ggplot2::xlab(expression(italic(T(y)) == median(italic(y))))
################################ ### format axis & facet text ### ################################ color_scheme_set("gray") p4 <- mcmc_trace(example_mcmc_draws(), pars = c("alpha", "sigma")) myfacets <- facet_bg(fill = "gray30", color = NA) + facet_text(face = "bold", color = "skyblue", size = 14) p4 + myfacets
########################## ### control tick marks ### ########################## p4 + myfacets + yaxis_text(FALSE) + yaxis_ticks(FALSE) + xaxis_ticks(size = 1, color = "skyblue")
############################## ### change plot background ### ############################## color_scheme_set("blue") # add grid lines ppc_stat(y, yrep) + grid_lines()
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# panel_bg vs plot_bg ppc_scatter_avg(y, yrep) + panel_bg(fill = "gray90")
ppc_scatter_avg(y, yrep) + plot_bg(fill = "gray90")
color_scheme_set("yellow") p5 <- ppc_scatter_avg(y, yrep, alpha = 1) p5 + panel_bg(fill = "gray20") + grid_lines(color = "white")
color_scheme_set("purple") ppc_dens_overlay(y, yrep[1:30, ]) + legend_text(size = 14) + legend_move(c(0.75, 0.5)) + plot_bg(fill = "gray90") + panel_bg(color = "black", fill = "gray99", size = 3)
############################################### ### superimpose a function on existing plot ### ############################################### # compare posterior of beta[1] to Gaussian with same posterior mean # and sd as beta[1] x <- example_mcmc_draws(chains = 4) dim(x)
#> [1] 250 4 4
purple_gaussian <- overlay_function( fun = dnorm, args = list(mean(x[,, "beta[1]"]), sd(x[,, "beta[1]"])), color = "purple", size = 2 ) color_scheme_set("gray") mcmc_hist(x, pars = "beta[1]") + purple_gaussian
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
mcmc_dens(x, pars = "beta[1]") + purple_gaussian