# binom.power

0th

Percentile

##### Power curves for binomial parameterizations

Uses Wald statistics to compute power curves for several parameterizations.

Keywords
models, htest, univar
##### Usage
binom.power(p.alt, n = 100, p = 0.5, alpha = 0.05, phi = 1, alternative = c("two.sided", "greater", "less"), method = c("cloglog", "logit", "probit", "asymp", "lrt", "exact"))
##### Arguments
p.alt
A vector of success probabilities under the alternative hypothesis.
n
A vector representing the number of independent trials in the binomial experiment.
p
A vector of success probabilities under the null hypothesis.
alpha
A vector of type-I error rates.
phi
A vector determining the overdispersion parameter for each binomial experiment.
alternative
Type of alternative hypothesis.
method
The method used to compute power.
##### Details

For derivations see doc/binom.pdf. p.alt, n, p, alpha, and phi can all be vectors. The length of each argument will be expanded to the longest length. The function assumes the lengths are equal or can be wrapped for multiple values.

##### Value

The estimated probability of detecting the difference between p.alt and p.

binom.confint, binom.bayes, binom.logit, binom.probit, binom.coverage
binom.power(0.95, alternative = "greater")