bayesplot-helpers {bayesplot}R Documentation

Convenience functions for adding or changing plot details

Description

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

Usage

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 ggplot2::geom_vline(), ggplot2::geom_hline(), and ggplot2::geom_abline(), respectively, to control the appearance of the line(s).

For functions ending in _bg, ... is passed to ggplot2::element_rect().

For functions ending in _text or _title, ... is passed to ggplot2::element_text().

For xaxis_ticks and yaxis_ticks, ... is passed to ggplot2::element_line().

For overlay_function, ... is passed to ggplot2::stat_function().

na.rm

A logical scalar passed to the appropriate geom (e.g. ggplot2::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 ggplot2::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 ggplot2::element_line().

Details

Add vertical, horizontal, and diagonal lines to plots

Control appearance of facet strips

Move legend, remove legend, or style the legend text

Control appearance of x-axis and y-axis features

Customize plot background

Superimpose a function on an existing plot

Value

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

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)
colnames(x)


###################################
### 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()

# 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)
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
mcmc_dens(x, pars = "beta[1]") + purple_gaussian


[Package bayesplot version 1.8.1 Index]