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

Description Usage Arguments Value See Also Examples

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

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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

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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.