binom.test
Exact Binomial Test
Description
Performs a hypothesis test to determine if a given sample has a particular proportion parameter.
Usage
binom.test(x, n, p = 0.5, alternative = c("two.sided", "less", "greater"),
conf.level = 0.95)
Arguments
x |
a numeric value that specifies the number of successes or a two-element numeric vector that specifies the number of successes and the number of failures.
If you specify a two-element numeric vector, the n argument is ignored and the number of trials is computed by finding the sum of the two elements.
|
n |
a numeric vector that specifies the number of trials. Ignored if argument x is a two-element numeric vector.
|
p |
probability of success to be tested.
|
alternative |
a character string that specifies the alternative hypothesis. To test the hypothesis, type one of the following:
two.sided | to specify that x and p are not equal. |
greater | to specify that x is greater than p. |
less | to specify that x is less than p.
|
Note: You only need to enter enough of the character string to create
a unique match for the value.
|
conf.level |
a numeric vector in the range [0, 1] that specifies the confidence level for the returned confidence interval.
|
Details
The exact probabilities are computed for the binomial distribution.
Value
returns a list with class attribute htest, that represents the result of binomial test:
statistic |
number of successes.
|
parameter |
number of trials.
|
p.value |
the p-value.
|
conf.int |
a confidence interval for the true probability of success.
The confidence level is recorded in the attribute conf.level.
|
estimate |
the sample estimate of the probability of success calculated by x / n.
|
null.value |
null hypothesis value of the probability of success.
|
alternative |
a character string that returns the alternative hypothesis (two.sided, greater, or less) as specified in the alternative argument.
|
method |
the string Exact binomial test.
|
data.name |
a character vector that describes the data used for the test.
The substitute of x or n (not evaluated value) is displayed in data.name.
|
References
Clopper, C. J. & Pearson, E. S. (1934).
"The use of confidence or fiducial limits illustrated in the case of the binomial."
Biometrika, 26(4), 404--413.
Conover, W. J. (1980).
Practical Nonparametric Statistics, 2nd ed.
Wiley, New York.
See Also
Examples
x <- rnorm(100)
y <- sum(x>0)
binom.test(y, 100)
y <- rnorm(100)
d <- x - y
binom.test(sum(d>0),length(d))
binom.test(c(23, 27), alternative = "less", conf.level = 0.90)
binom.test(23, 23 + 27, alternative = "less", conf.level = 0.90) # the same