binom.test(stats)
binom.test()所属R语言包：stats
Exact Binomial Test
精确二项式测试
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Performs an exact test of a simple null hypothesis about the probability of success in a Bernoulli experiment.
执行一个简单的零假设在伯努利实验的成功概率的精确检验。
用法----------Usage----------
binom.test(x, n, p = 0.5,
alternative = c("two.sided", "less", "greater"),
conf.level = 0.95)
参数----------Arguments----------
参数：x
number of successes, or a vector of length 2 giving the numbers of successes and failures, respectively.
成功，或向量的长度为2的成功和失败的数字，分别。
参数：n
number of trials; ignored if x has length 2.
试验;忽略x长度为2。
参数：p
hypothesized probability of success.
假设成功的概率。
参数：alternative
indicates the alternative hypothesis and must be one of "two.sided", "greater" or "less". You can specify just the initial letter.
表示替代假说，必须是一个"two.sided"，"greater"或"less"。您可以指定只的首字母。
参数：conf.level
confidence level for the returned confidence interval.
返回的置信区间的置信水平。
Details
详情----------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.
置信区间，获得了第一在Clopper和Pearson（1934）给予程序。这保证了置信水平是至少conf.level，但一般不给置信区间的最短长度。
值----------Value----------
A list with class "htest" containing the following components:
一类"htest"包含以下组件的列表：
参数：statistic
the number of successes.
成功的次数。
参数：parameter
the number of trials.
试验次数。
参数：p.value
the p-value of the test.
p值的测试。
参数：conf.int
a confidence interval for the probability of success.
一个成功的概率置信区间。
参数：estimate
the estimated probability of success.
估计成功的概率。
参数：null.value
the probability of success under the null, p.
空下的成功概率，p。
参数：alternative
a character string describing the alternative hypothesis.
字符串描述替代假说。
参数：method
the character string "Exact binomial test".
字符串"Exact binomial test"。
参数：data.name
a character string giving the names of the data.
字符串提供的数据的名称。
参考文献----------References----------
The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika, 26, 404–413.
Practical nonparametric statistics. New York: John Wiley & Sons. Pages 97–104.
Nonparametric Statistical Methods. New York: John Wiley & Sons. Pages 15–22.
参见----------See Also----------
prop.test for a general (approximate) test for equal or given proportions.
prop.test一般（近似）测试或等于给定的比例。
举例----------Examples----------
## Conover (1971), p. 97f.[＃康诺弗（1971年），P。 97f。]
## Under (the assumption of) simple Mendelian inheritance, a cross[＃下（假设）简单的孟德尔遗传，交叉]
## between plants of two particular genotypes produces progeny 1/4 of[＃之间的两个特定基因型的植物产生的后代的1/4]
## which are "dwarf" and 3/4 of which are "giant", respectively.[＃这是“小矮人”和3/4，其中有“巨人”，分别。]
## In an experiment to determine if this assumption is reasonable, a[＃在一项实验，以确定如果这个假设是合理的，]
## cross results in progeny having 243 dwarf and 682 giant plants.[＃跨中有243矮和682个巨型植物后代的结果。]
## If "giant" is taken as success, the null hypothesis is that p =[＃如果“巨人”作为成功，零假设是P =]
## 3/4 and the alternative that p != 3/4.[＃3/4和替代，P！= 3/4。]
binom.test(c(682, 243), p = 3/4)
binom.test(682, 682 + 243, p = 3/4) # The same.[相同。]
## => Data are in agreement with the null hypothesis.[＃=>数据与零假设的协议。]
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
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发表于 2012-2-16 19:20:24
