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

## Description

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

## Usage

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

## Arguments

 `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.

## Value

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

## Author(s)

Titus von der Malsburg <[email protected]>

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```# 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) ```

tmalsburg/binomialCRIs documentation built on May 13, 2018, 12:49 a.m.