binom.ppow: Compute the power of a binomial experiment using pilot data.

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

Description

binom.ppow computes (through simulation) the power of a binomial experiment with a specified sample size, using pilot data.

Usage

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

Arguments

n	sample size.
pilotdata	a list with numerical or categorical pilot data. Largest number of second category is treated as 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.ppow(n=16, pilotdata = (1, 1, 0, 0, 1, 1), p=0.8)
binom.ppow(n=16, pilotdata = ("a", "b", "b", "b"), p=0.8, alternative="greater")
binom.ppow(n=16, pilotdata = ("a", "b", "b", "b"), r=5000, nullp=0.25, alternative="greater")

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