Power curves for binomial parameterizations
Uses Wald statistics to compute power curves for several parameterizations.
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"))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. |
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.
The estimated probability of detecting the difference between
p.alt and p.
Sundar Dorai-Raj (sdorairaj@gmail.com)
binom.power(0.95, alternative = "greater")
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