binom.explore: Explore the power of a binomial experiment under different...
In julianje/mcpa: Intuitive power analyses through monte carlo simulations

Description Usage Arguments Value See Also Examples

binom.explore computes (through simulation) the power of a binomial experiment under different sample sizes.

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

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

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

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

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binom.explore(lown=16, topn=24, p=0.8)
binom.explore(lown=16, topn=24, p=0.8, alternative="greater")
binom.explore(lown=16, topn=24, p=0.6, r=5000, nullp=0.25)