binom.test
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, C. J. & Pearson, E. S. (1934). The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika, 26, 404–413. doi: 10.2307/2331986.
William J. Conover (1971), Practical nonparametric statistics. New York: John Wiley & Sons. Pages 97–104.
Myles Hollander & Douglas A. Wolfe (1973), Nonparametric Statistical Methods. New York: John Wiley & Sons. 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.
Copyright (©) 1999–2012 R Foundation for Statistical Computing.
Licensed under the GNU General Public License.