binomial_prob: Calculates the posterior probability that the probability...
In tmalsburg/binomialCRIs: Calculate and Plot Binomial Credible Intervals

Description Usage Arguments Value Author(s) Examples

Calculates the posterior probability that the probability parameter underlying a binomially distributed outcome is in a specified interval.

1 2	binomial_prob(n_successes, n_trials, prob_lower = 0, prob_upper = 1, prior_shape1 = 1, prior_shape2 = 1)

`n_successes`	The number of successes.
`n_trials`	The total number of trials.
`prob_lower`	The lower end point of the interval.
`prob_upper`	The upper end point of the interval.
`prior_shape1`	The shape1 parameter of the Beta distribution defining the prior. The default values shape1=1 and shape2=1 define a flat prior assigning equal probability density to all possible parameter values.
`prior_shape2`	The shape2 parameter of the Beta distribution defining the prior. The default values shape1=1 and shape2=1 define a flat prior assigning equal probability density to all possible parameter values.

The posterior probability that the parameter value lies in the specified interval.

Titus von der Malsburg <malsburg@uni-potsdam.de>

# Probability of parameter being larger than 0.5 after seeing 6/9
# successes with flat prior:
binomial_prob(6, 9, 0.5)

# Probability of parameter being smaller than 0.5 after seeing 6/9
# successes with prior assuming one earlier success and one
# failure:
binomial_prob(6, 9, prob_upper=0.5, prior_shape1=2, prior_shape2=2)

# Probability of parameter being larger than 0.5 and smaller than
# 0.75 after seeing 6/9 successes with flat prior:
binomial_prob(6, 9, 0.5, 0.75)