| binom.test {stats} | R Documentation |
Exact Binomial Test
Description
Performs an exact test of a simple null hypothesis about the probability of success in a Bernoulli experiment.
Usage
binom.test(x, n, p = 0.5,
alternative = c("two.sided", "less", "greater"),
conf.level = 0.95)
Arguments
x |
number of successes, or a vector of length 2 giving the numbers of successes and failures, respectively. |
n |
number of trials; ignored if |
p |
hypothesized probability of success. |
alternative |
indicates the alternative hypothesis and must be
one of |
conf.level |
confidence level for the returned confidence interval. |
Details
Confidence intervals are obtained by a procedure first given in
Clopper and Pearson (1934).
This guarantees that the confidence level
is at least conf.level, but in general does not give the
shortest-length confidence intervals.
Value
A list with class "htest" containing the following components:
statistic |
the number of successes. |
parameter |
the number of trials. |
p.value |
the p-value of the test. |
conf.int |
a confidence interval for the probability of success. |
estimate |
the estimated probability of success. |
null.value |
the probability of success under the null,
|
alternative |
a character string describing the alternative hypothesis. |
method |
the character string |
data.name |
a character string giving the names of the data. |
References
Clopper CJ, Pearson ES (1934). “The Use of Confidence or Fiducial Limits Illustrated in the Case of the Binomial.” Biometrika, 26(4), 404–413. doi:10.1093/biomet/26.4.404.
Conover WJ (1971).
Practical Nonparametric Statistics.
Wiley.
ISBN 9780471168515.
Pages 97–104.
Hollander M, Wolfe DA (1973).
Nonparametric Statistical Methods.
John Wiley & Sons, New York.
ISBN 9780471406358.
Pages 15–22.
See Also
prop.test for a general (approximate) test for equal or
given proportions.
Examples
## Conover (1971), p. 97f.
## Under (the assumption of) simple Mendelian inheritance, a cross
## between plants of two particular genotypes produces progeny 1/4 of
## which are "dwarf" and 3/4 of which are "giant", respectively.
## In an experiment to determine if this assumption is reasonable, a
## cross results in progeny having 243 dwarf and 682 giant plants.
## If "giant" is taken as success, the null hypothesis is that p =
## 3/4 and the alternative that p != 3/4.
binom.test(c(682, 243), p = 3/4)
binom.test(682, 682 + 243, p = 3/4) # The same.
## => Data are in agreement with the null hypothesis.