| % File src/library/stats/man/binom.test.Rd |
| % Part of the R package, https://www.R-project.org |
| % Copyright 1995-2018 R Core Team |
| % Distributed under GPL 2 or later |
| |
| \name{binom.test} |
| \alias{binom.test} |
| \title{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{ |
| \item{x}{number of successes, or a vector of length 2 giving the |
| numbers of successes and failures, respectively.} |
| \item{n}{number of trials; ignored if \code{x} has length 2.} |
| \item{p}{hypothesized probability of success.} |
| \item{alternative}{indicates the alternative hypothesis and must be |
| one of \code{"two.sided"}, \code{"greater"} or \code{"less"}. |
| You can specify just the initial letter.} |
| \item{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 \code{conf.level}, but in general does not give the |
| shortest-length confidence intervals. |
| } |
| \value{ |
| A list with class \code{"htest"} containing the following components: |
| \item{statistic}{the number of successes.} |
| \item{parameter}{the number of trials.} |
| \item{p.value}{the p-value of the test.} |
| \item{conf.int}{a confidence interval for the probability of success.} |
| \item{estimate}{the estimated probability of success.} |
| \item{null.value}{the probability of success under the null, |
| \code{p}.} |
| \item{alternative}{a character string describing the alternative |
| hypothesis.} |
| \item{method}{the character string \code{"Exact binomial test"}.} |
| \item{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. |
| \emph{Biometrika}, \bold{26}, 404--413. |
| \doi{10.2307/2331986}. |
| |
| William J. Conover (1971), |
| \emph{Practical nonparametric statistics}. |
| New York: John Wiley & Sons. |
| Pages 97--104. |
| |
| Myles Hollander & Douglas A. Wolfe (1973), |
| \emph{Nonparametric Statistical Methods.} |
| New York: John Wiley & Sons. |
| Pages 15--22. |
| } |
| \seealso{ |
| \code{\link{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. |
| } |
| \keyword{htest} |