hyperg(VGAM)
hyperg()所属R语言包：VGAM
Hypergeometric Family Function
超几何家庭功能
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Family function for a hypergeometric distribution where either the number of white balls or the total number of white and black balls are unknown.
家庭功能的超几何分布，在以下情况下白球的数目或总数的白色和黑色的球是未知的。
用法----------Usage----------
hyperg(N = NULL, D = NULL, lprob = "logit", earg = list(), iprob = NULL)
参数----------Arguments----------
参数：N
Total number of white and black balls in the urn. Must be a vector with positive values, and is recycled, if necessary, to the same length as the response. One of N and D must be specified.
瓮里的白球和黑球的总数。必须是一个具有正的值的矢量，被回收时，如有必要，作为响应的相同的长度。其中的N和D必须指定。
参数：D
Number of white balls in the urn. Must be a vector with positive values, and is recycled, if necessary, to the same length as the response. One of N and D must be specified.
瓮里的白球的数量。必须是一个具有正的值的矢量，被回收时，如有必要，作为响应的相同的长度。其中的N和D必须指定。
参数：lprob
Link function for the probabilities. See Links for more choices.
Link功能的概率。见Links更多的选择。
参数：earg
List. Extra argument for the link. See earg in Links for general information.
列表。额外的参数的链接。见earg中Links的一般信息。
参数：iprob
Optional initial value for the probabilities. The default is to choose initial values internally.
可选的初始值的概率。默认值是内部选择的初始值。
Details
详细信息----------Details----------
Consider the scenario from dhyper where there are N=m+n balls in an urn, where m are white and n are black. A simple random sample (i.e., without replacement) of k balls is taken. The response here is the sample proportion of white balls. In this document, N is N=m+n, D is m (for the number of “defectives”, in quality control terminology, or equivalently, the number of marked individuals). The parameter to be estimated is the population proportion of white balls, viz. prob = m/(m+n).
考虑的情况下，从dhyper有N=m+n在瓮中的球，其中m是白色和n是黑的。一个简单的随机抽样（即，无需更换）k球的。这里的反应是白球的样本比例。在这份文件中，N是N=m+n，D是m（的“缺陷”的数量，质量控制术语，或者等价地，一些显着的个人）。要估计的参数是白球，即人口的比例。 prob = m/(m+n)。
Depending on which one of N and D is inputted, the estimate of the other parameter can be obtained from the equation prob = m/(m+n), or equivalently, prob = D/N. However, the log-factorials are computed using lgamma and both m and n are not restricted to being integer. Thus if an integer N is to be estimated, it will be necessary to evaluate the likelihood function at integer values about the estimate, i.e., at trunc(Nhat) and ceiling(Nhat) where Nhat is the (real) estimate of N.
取决于哪一个N和D被输入，其他参数的估计可以以下方式获得从等式prob = m/(m+n)，或等价地，prob = D/N。然而，对数的阶乘计算lgamma都m和n不限制为整数。因此，如果一个整数N是要估计，将要评价的似然函数在整数有关的估计值，即，在trunc(Nhat)和ceiling(Nhat)其中Nhat （实际）估计N。
值----------Value----------
An object of class "vglmff" (see vglmff-class). The object is used by modelling functions such as vglm, vgam, rrvglm, cqo, and cao.
类的一个对象"vglmff"（见vglmff-class）。该对象被用于建模功能，如vglm，vgam，rrvglm，cqo和cao。
警告----------Warning ----------
No checking is done to ensure that certain values are within range, e.g., k <= N.
未进行任何检查，以确保特定值范围内的，例如，k <= N。
注意----------Note----------
The response can be of one of three formats: a factor (first level taken as success), a vector of proportions of success, or a 2-column matrix (first column = successes) of counts. The argument weights in the modelling function can also be specified. In particular, for a general vector of proportions, you will need to specify weights because the number of trials is needed.
响应可以是三种格式之一：一个因素（视为成功的第一电平），在成功的比例的向量，或2列的矩阵（第一列=成功）的计数。参数weights的建模功能也可以指定。特别是，对于一个向量的比例，你会需要指定weights，因为审判的需要。
（作者）----------Author(s)----------
Thomas W. Yee
参考文献----------References----------
Statistical Distributions, New York: Wiley-Interscience, Third edition.
参见----------See Also----------
dhyper, binomialff.
dhyper，binomialff。
实例----------Examples----------
nn = 100
m = 5 # number of white balls in the population[在人群中的白球数]
k = rep(4, len = nn) # sample sizes[样本量]
n = 4 # number of black balls in the population[在人群中的黑球数]
y = rhyper(nn = nn, m = m, n = n, k = k)
yprop = y / k # sample proportions[样本比例]
# N is unknown, D is known. Both models are equivalent:[N是未知的，D是已知的。这两个模型是等价的：]
fit = vglm(cbind(y,k-y) ~ 1, hyperg(D = m), trace = TRUE, crit = "c")
fit = vglm(yprop ~ 1, hyperg(D=m), weight = k, trace = TRUE, crit = "c")
# N is known, D is unknown. Both models are equivalent:[N已知，D是未知的。这两个模型是等价的：]
fit = vglm(cbind(y,k-y) ~ 1, hyperg(N = m+n), trace = TRUE, crit = "l")
fit = vglm(yprop ~ 1, hyperg(N = m+n), weight = k, trace = TRUE, crit = "l")
coef(fit, matrix = TRUE)
Coef(fit) # Should be equal to the true population proportion[真正的人口比例应该是平等的]
unique(m / (m+n)) # The true population proportion[真正的人口比例]
fit@extra
head(fitted(fit))
summary(fit)
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。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
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发表于 2012-10-1 15:39:01
