binom.pexplore: Explore the power of a binomial experiment under different...

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

Description

binom.pexplore computes (through simulation) the power of a binomial experiment under different sample sizes. Rather than taking a probability of success (like binom.explore), binom.pexplore takes a vector of pilot data.

Usage

binom.pexplore(lown, topn, pilotdata, r = 10000,
  alternative = c("two.sided", "less", "greater"), alpha = 0.05,
  nullp = 0.5, conf.level = 0.95, plotit = TRUE)

Arguments

lown	smallest sample size to explore.
topn	largest sample size to explore.
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.
plotit	logical (default=TRUE) value. Function generates a plot when TRUE and returns a data frame otherwise.

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.pexplore(lown=16, topn=24, pilotdata = (1, 1, 0, 0, 1, 1), p=0.8)
binom.pexplore(lown=16, topn=24, pilotdata = ("a", "b", "b", "b"), p=0.8, alternative="greater")
binom.pexplore(lown=16, topn=24, pilotdata = ("a", "b", "b", "b"), r=5000, nullp=0.25, alternative="greater")

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