Learn R Programming

evidence (version 0.8.10)

B1prop: Bayesian analysis of the binomial parameter for one sample.

Description

This function computes the posterior distribution of the binomial probability \(\pi\) when given the number of ``successes'' and the sample size, as well as one of a choice of priors. A plot of the posterior distribution is produced with the 95% credible interval of \(\pi\).

Usage

B1prop(s, n, p = 0.5, alpha = 0.05, prior = c("uniform", "near_0.5",
  "not_near_0.5", "near_0", "near_1", "custom"), params = NULL)

Arguments

s

the number of sampling units with the feature

n

the number of sampling units examined

p

an optional hypothesized probability

alpha

1 - alpha is the desired level of credibility of a credible interval

prior

one of: "uniform", "near_0.5", "not_near_0.5", "near_0", "near_1", "custom", which are all beta distributions with appropriate parameter values. Note that if prior="custom" the following argument has to be supplied:

params

a vector with the a and b parameters of the custom beta prior

Value

the posterior probability

References

van Hulst, R. 2018. Evaluating Scientific Evidence. ms.

See Also

B2props

prop.test

Examples

Run this code
# NOT RUN {
B1prop(13, 100, .1, prior="near_0")
# }

Run the code above in your browser using DataLab