PlotDistCountBinomial {WVPlots}

Plot count data with a theoretical binomial

Description

Compares empirical count data to a binomial distribution

Usage

PlotDistCountBinomial(
  frm,
  xvar,
  trial_size,
  title,
  ...,
  p = NULL,
  limit_to_observed_range = FALSE,
  count_color = "black",
  binom_color = "blue"
)

Arguments

frm

data frame to get values from

xvar

column of frm that counts the number of successes for each trial

trial_size

the number of "coin flips" in a trial

title

title to place on plot

...

no unnamed argument, added to force named binding of later arguments.

p

mean of the binomial. If NULL, use empirical mean

limit_to_observed_range

If TRUE, limit plot to observed counts

count_color

color of empirical distribution

binom_color

color of theoretical binomial

Details

This function is useful for comparing the number of successes that occur in a series of trials, all of the same size, to a binomial of a given success-probability.

Plots the empirical distribution of successes, and a theoretical matching binomial. If the mean of the binomial, p, is given, the binomial with success-probability p is plotted. Otherwise, p is taken to be the pooled success rate of the data: sum(frm[[xvar]]) / (trial_size*nrow(frm)). The mean of the binomial is reported in the subtitle of the plot (to three significant figures).

If limit_to_observed_range is TRUE, the range of the plot will only cover the range of the empirical data. Otherwise, the range of the plot will be 0:trial_size (the default).

See Also

PlotDistHistBeta, PlotDistDensityBeta,

Examples

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

set.seed(23590)
class_size = 35
nclasses = 100
true_frate = 0.4
fdata = data.frame(n_female = rbinom(nclasses, class_size, true_frate), stringsAsFactors = FALSE)

title = paste("Distribution of count of female students, class size =", class_size)
# compare to empirical p
PlotDistCountBinomial(fdata, "n_female", class_size, title)

if(FALSE) {
  # compare to theoretical p of 0.5
  PlotDistCountBinomial(fdata, "n_female", class_size, title,
                        p = 0.5)

  # Example where the distribution is not of a true single binomial
  fdata2 = rbind(data.frame(n_female = rbinom(50, class_size, 0.25)),
                data.frame(n_female = rbinom(10, class_size, 0.60)),
                stringsAsFactors = FALSE )
  PlotDistCountBinomial(fdata2, "n_female", class_size, title)
}