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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(...)
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.
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().
A logical scalar passed to the appropriate geom (e.g.
ggplot2::geom_vline()). The default is TRUE.
The probability mass (in [0,1]) to include in the interval.
Should the median also be included in addition to the lower and upper bounds of the interval?
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().
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.
Passed to ggplot2::element_line().
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.
vline_at() and hline_at() return an object created by either
ggplot2::geom_vline() or ggplot2::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 ggplot2::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\
of x, as well as the median (if med=TRUE).
facet_text() returns ggplot2 theme objects that can be added to an
existing plot (ggplot object) to format the text in facet strips.
facet_bg() can be added to a plot to change the background of the facet strips.
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 or remove it.
legend_text() works much like facet_text() but for the legend.
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 ggplot2::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.
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.
overlay_function() is a simple wrapper for ggplot2::stat_function() but
with the inherit.aes argument fixed to FALSE. Fixing inherit.aes=FALSE
will avoid potential errors due to the ggplot2::aes()thetic mapping used by
certain bayesplot plotting functions.
theme_default() for the default ggplot theme used by
bayesplot.
# NOT RUN {
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
# }
# NOT RUN {
# 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)
# }
# NOT RUN {
# 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
# }
# NOT RUN {
##########################
### control tick marks ###
##########################
p4 +
myfacets +
yaxis_text(FALSE) +
yaxis_ticks(FALSE) +
xaxis_ticks(size = 1, color = "skyblue")
# }
# NOT RUN {
##############################
### 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")
# }
# NOT RUN {
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)
# }
# NOT RUN {
###############################################
### 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
# }
# NOT RUN {
mcmc_dens(x, pars = "beta[1]") + purple_gaussian
# }
# NOT RUN {
# }
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