PlotDistHistBeta {WVPlots}

Plot empirical rate data as a histogram plus matching beta

Description

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

Usage

PlotDistHistBeta(
  frm,
  xvar,
  title,
  ...,
  bins = 30,
  hist_color = "darkgray",
  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

bins

passed to geom_histogram(). Default: 30

hist_color

color of empirical histogram

beta_color

color of matching theoretical beta

mean_color

color of mean line

sd_color

color of 1-standard devation lines (can be NULL)

Details

Plots the histogram of the empirical distribution and the density of the matching beta distribution. Also plots the mean and plus/minus one standard deviation.

The number of bins for the histogram defaults to 30. The binwidth can also be passed in instead of the number of bins.

Value

ggplot2 plot

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, n_gray/herd_size)

title = "Observed prevalence of gray horses in population"

PlotDistHistBeta(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
# PlotDistHistBeta(hdata, "rate_gray", title,
#                     sd_color=NULL)