Description Usage Arguments Details Value Examples
bayes.binom.test
estimates the relative frequency of success using
Bayesian estimation and is intended as a replacement for
binom.test
.
1 2  bayes.binom.test(x, n, comp.theta = 0.5, alternative = NULL,
cred.mass = 0.95, n.iter = 15000, progress.bar = "none", p, conf.level)

x 
number of successes, or a vector of length 2 giving the numbers of successes and failures, respectively. 
n 
number of trials; ignored if x has length 2. 
comp.theta 
a fixed relative frequency of success to compare with the
estimated relative frequency of success. This argument fills a similar role
as 
alternative 
ignored and is only retained in order to mantain
compatibility with 
cred.mass 
the amount of probability mass that will be contained in
reported credible intervals. This argument fills a similar role as

n.iter 
The number of iterations to run the MCMC sampling. 
progress.bar 
The type of progress bar. Possible values are "text", "gui", and "none". 
p 
same as 
conf.level 
same as 
Given data on the number of successes and failures bayes.binom.test
estimates θ, the relative frequency of success, assuming the
following model:
x ~ Binomial(θ, n)
θ ~ Beta(1, 1)
Here the prior on θ is a noninformative Beta(1, 1) distribution which is identical to a Uniform(0, 1) distribution. By plot
ing and looking at a
summary
of the object returned by bayes.binom.test
you can get
more information about the shape of the posterior and the posterior
predictive distribution. model.code
prints out the
corresponding R code underlying bayes.binom.test
which can be
copynpasted into an R script and modified, for example, changing the prior
on θ.
A list of class bayes_binom_test
that contains information
about the analysis. It can be further inspected using the functions
summary
, plot
, diagnostics
and
model.code
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  # A college dormitory recently sponsored a taste comparison between
# two major soft drinks. Of the 64 students who participated, 39 selected
# brand A, and only 25 selected brand B. Example from
# http://www.elderlab.yorku.ca/~aaron/Stats2022/BinomialTest.htm
bayes.binom.test(x = 39, n = 64)
# Save the return value in order to inspect the model result further.
fit < bayes.binom.test(x = 39, n = 64, cred.mass=0.8)
summary(fit)
plot(fit)
# MCMC diagnostics (should not be necessary for such a simple model)
diagnostics(fit)
# Print out the R code to run the model. This can be copy n' pasted into
# an Rscript and further modified.
model.code(fit)

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