binom.pow: Compute the power of a binomial experiment.

In julianje/mcpa: Intuitive power analyses through monte carlo simulations

Description

binom.pow computes (via simulation) the power of a binomial experiment with a specified sample size and probability of success.

Usage

binom.pow(n, p, r = 10000, alternative = c("two.sided", "less", "greater"),
  alpha = 0.05, nullp = 0.5, conf.level = 0.95)

Arguments

n	sample size.
p	predicted probability of success.
r	number of simulations to compute power.
alternative	type of alternative hypothesis in binomial test. Must be "two.sided" (default), "greater", or "less".
alpha	significance threshhold.
nullp	probability of success in null hypothesis.
conf.level	size of confidence intervals.

Value

The probability of finding p < α with the experiment description and a 95

See Also

binom.power, binom.ppow, binom.explore, and binom.pexplore.

Examples

binom.pow(n=16, p=0.8)
binom.pow(n=16, p=0.8, alternative="greater")
binom.pow(n=32, p=0.6, r=5000, nullp=0.25)

julianje/mcpa documentation built on May 13, 2019, 6:14 p.m.