Bayesian analysis of one-sample binomial data using conjugate beta priors

Usage

binom_bayes(
  x,
  n,
  a = 1,
  b = 1,
  level = 0.95,
  null,
  plot = FALSE,
  add = FALSE,
  xlab = "p",
  ylab = "Posterior density",
  col = "blue",
  ...
)

Arguments

x: Number of "successes"
n: Number of trials
a: 'alpha' parameter for beta prior. Default: uniform prior
b: 'beta' parameter for beta prior. Default: uniform prior
level: For posterior interval; .95 for a 95% credible interval
null: Point null to evaluate as a kind of hypothesis test
plot: Draw a plot? Default: false
add: Add to existing plot? Default: false
xlab: xlab for plot
ylab: ylab for plot
col: color of density line for plot
...: Additional arguments to plot()

Examples

binom_bayes(10, 16)
#> Sample proportion: 0.625 
#> Posterior mean: 0.611 
#> Posterior mode: 0.625 
#> Posterior SD: 0.112 
#> 95% central interval: (0.383, 0.816)
#> 95% HPD interval: (0.392, 0.823)
binom_bayes(0, 21, plot=TRUE)

#> Sample proportion: 0.000 
#> Posterior mean: 0.043 
#> Posterior mode: 0.000 
#> Posterior SD: 0.042 
#> 95% central interval: (0.001, 0.154)
#> 95% HPD interval: (0.000, 0.127)