Description Usage Arguments Value See Also Examples
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.
1 2 3 | 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)
|
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 " |
alpha |
significance threshhold. |
nullp |
probability of success in null hypothesis. |
conf.level |
size of confidence intervals. |
plotit |
logical (default= |
The probability of finding p < α with the experiment description and a 95
binom.power
, binom.ppow
, binom.explore
, and binom.pexplore
.
1 2 3 | 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")
|
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