Medians and central interval estimates of yrep
with y
overlaid.
See the Plot Descriptions section, below.
ppc_intervals( y, yrep, x = NULL, ..., prob = 0.5, prob_outer = 0.9, alpha = 0.33, size = 1, fatten = 2.5 ) ppc_intervals_grouped( y, yrep, x = NULL, group, ..., facet_args = list(), prob = 0.5, prob_outer = 0.9, alpha = 0.33, size = 1, fatten = 2.5 ) ppc_ribbon( y, yrep, x = NULL, ..., prob = 0.5, prob_outer = 0.9, alpha = 0.33, size = 0.25, y_draw = c("line", "points", "both") ) ppc_ribbon_grouped( y, yrep, x = NULL, group, ..., facet_args = list(), prob = 0.5, prob_outer = 0.9, alpha = 0.33, size = 0.25, y_draw = c("line", "points", "both") ) ppc_intervals_data( y, yrep, x = NULL, group = NULL, ..., prob = 0.5, prob_outer = 0.9 ) ppc_ribbon_data( y, yrep, x = NULL, group = NULL, ..., prob = 0.5, prob_outer = 0.9 )
y | A vector of observations. See Details. |
---|---|
yrep | An |
x | A numeric vector to use as the x-axis
variable. For example, |
... | Currently unused. |
prob, prob_outer | Values between |
alpha, size, fatten | Arguments passed to geoms. For ribbon plots |
group | A grouping variable of the same length as |
facet_args | A named list of arguments (other than |
y_draw | For ribbon plots only, a string specifying how to draw |
The plotting functions return a ggplot object that can be further
customized using the ggplot2 package. The functions with suffix
_data()
return the data that would have been drawn by the plotting
function.
ppc_intervals(), ppc_ribbon()
100*prob
% central intervals for yrep
at each x
value. ppc_intervals()
plots intervals as vertical bars with points
indicating yrep
medians and darker points indicating observed
y
values. ppc_ribbon()
plots a ribbon of connected intervals
with a line through the median of yrep
and a darker line connecting
observed y
values. In both cases an optional x
variable can
also be specified for the x-axis variable.Depending on the number of observations and the variability in the
predictions at different values of x
, one of these plots may be easier
to read than the other.
ppc_intervals_grouped(), ppc_ribbon_grouped()
Same as ppc_intervals()
and ppc_ribbon()
, respectively, but a
separate plot (facet) is generated for each level of a grouping variable.
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)
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). Bayesian Data Analysis. Chapman & Hall/CRC Press, London, third edition. (Ch. 6)
Other PPCs:
PPC-censoring
,
PPC-discrete
,
PPC-distributions
,
PPC-errors
,
PPC-loo
,
PPC-overview
,
PPC-scatterplots
,
PPC-test-statistics
y <- rnorm(50) yrep <- matrix(rnorm(5000, 0, 2), ncol = 50) color_scheme_set("brightblue") ppc_intervals(y, yrep)ppc_ribbon(y, yrep)ppc_ribbon(y, yrep, y_draw = "points")# \dontrun{ ppc_ribbon(y, yrep, y_draw = "both")# } ppc_intervals(y, yrep, size = 1.5, fatten = 0) # remove the yrep point estimatescolor_scheme_set("teal") year <- 1950:1999 ppc_intervals(y, yrep, x = year, fatten = 1) + ggplot2::xlab("Year")color_scheme_set("pink") year <- rep(2000:2009, each = 5) group <- gl(5, 1, length = 50, labels = LETTERS[1:5]) ppc_ribbon_grouped(y, yrep, x = year, group, y_draw = "both") + ggplot2::scale_x_continuous(breaks = pretty)ppc_ribbon_grouped(y, yrep, x = year, group, facet_args = list(scales = "fixed")) + xaxis_text(FALSE) + xaxis_ticks(FALSE) + panel_bg(fill = "gray20")# get the data frames used to make the ggplots ppc_dat <- ppc_intervals_data(y, yrep, x = year, prob = 0.5) ppc_group_dat <- ppc_intervals_data(y, yrep, x = year, group = group, prob = 0.5) # \dontrun{ library("rstanarm") fit <- stan_glmer(mpg ~ wt + (1|cyl), data = mtcars, refresh = 0) yrep <- posterior_predict(fit) color_scheme_set("purple") ppc_intervals(y = mtcars$mpg, yrep = yrep, x = mtcars$wt, prob = 0.8) + panel_bg(fill="gray90", color = NA) + grid_lines(color = "white")ppc_ribbon(y = mtcars$mpg, yrep = yrep, x = mtcars$wt, prob = 0.6, prob_outer = 0.8)ppc_ribbon_grouped(y = mtcars$mpg, yrep = yrep, x = mtcars$wt, group = mtcars$cyl)color_scheme_set("gray") ppc_intervals(mtcars$mpg, yrep, prob = 0.5) + ggplot2::scale_x_continuous( labels = rownames(mtcars), breaks = 1:nrow(mtcars) ) + xaxis_text(angle = -70, vjust = 1, hjust = 0) + xaxis_title(FALSE)# }