value_remaining {bandit}

value_remaining

Description

Compute the "value_remaining" in the binomial bandits

Usage

value_remaining(x, n, alpha = 1, beta = 1, ndraws = 10000)

Arguments

x

as in prop.test, a vector of the number of successes

n

as in prop.test, a vector of the number of trials

alpha

shape parameter alpha for the prior beta distribution.

beta

shape parameter beta for the prior beta distribution.

ndraws

number of random draws from the posterior

Value

value_remaining distribution; the distribution of improvement amounts that another arm might have over the current best arm

Author(s)

Thomas Lotze and Markus Loecher

Examples

x=c(10,20,30,80)
n=c(100,102,120,240)
vr = value_remaining(x, n)
hist(vr)
best_arm = which.max(best_binomial_bandit(x, n))
# "potential value" remaining in the experiment
potential_value = quantile(vr, 0.95)
paste("Were still unsure about the CvR for the best arm (arm ", best_arm,
	"), but whatever it is, one of the other arms might beat it by as much as ",
	round(potential_value*100, 4), " percent.", sep="")