| binom.test {stats} | R Documentation |
Performs an exact test of a simple null hypothesis about the probability of success in a Bernoulli experiment.
binom.test(x, n, p = 0.5,
alternative = c("two.sided", "less", "greater"),
conf.level = 0.95)
x |
number of successes, or a vector of length 2 giving the numbers of successes and failures, respectively. |
n |
number of trials; ignored if x has 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. |
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.
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,
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. |
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 inference. New York: John Wiley & Sons. Pages 15–22.
prop.test for a general (approximate) test for equal or
given proportions.
## 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.