binom.test
Exact Binomial Test
Performs an exact test of a simple null hypothesis about the probability of success in a Bernoulli experiment.
- Keywords
- htest
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
xhas length 2. - p
- hypothesized probability of success.
- alternative
- indicates the alternative hypothesis and must be
one of
"two.sided","greater"or"less". You can specify just the initial letter. - 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
- 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,
p. - alternative
- a character string describing the alternative hypothesis.
- method
- the character string
"Exact binomial test". - data.name
- a character string giving the names of the data.
"htest" containing the following components:
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.
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
library(stats)
## 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.