PlotDistDensityBeta {WVPlots}

Plot empirical rate data as a density with the matching beta distribution

Description

Compares empirical rate data to a beta distribution with the same mean and standard deviation.

Usage

PlotDistDensityBeta(
  frm,
  xvar,
  title,
  ...,
  curve_color = "lightgray",
  beta_color = "blue",
  mean_color = "blue",
  sd_color = "darkgray"
)

Arguments

frm

data frame to get values from

xvar

name of the independent (input or model) column in frame

title

title to place on plot

...

force later arguments to bind by name

curve_color

color for empirical density curve

beta_color

color for matching theoretical beta

mean_color

color for mean line

sd_color

color for 1-standard deviation lines (can be NULL)

Details

Plots the empirical density, the theoretical matching beta, the mean value, and plus/minus one standard deviation from the mean.

Examples

if (requireNamespace('data.table', quietly = TRUE)) {
	# don't multi-thread during CRAN checks
		data.table::setDTthreads(1)
}

set.seed(52523)
N = 100
pgray = 0.1  # rate of gray horses in the population
herd_size = round(runif(N, min=25, 50))
ngray = rbinom(N, herd_size, pgray)
hdata = data.frame(n_gray=ngray, herd_size=herd_size)

# observed rate of gray horses in each herd
hdata$rate_gray = with(hdata, ngray/herd_size)

title = "Observed prevalence of gray horses in population"

PlotDistDensityBeta(hdata, "rate_gray", title) +
  ggplot2::geom_vline(xintercept = pgray, linetype=4, color="maroon") +
  ggplot2::annotate("text", x=pgray+0.01, y=0.01, hjust="left",
                    label = paste("True prevalence =", pgray))

# # no sd lines
# PlotDistDensityBeta(hdata, "rate_gray", title,
#                     sd_color=NULL)