beta.ab(VGAM)
beta.ab()所属R语言包：VGAM
The Two-parameter Beta Distribution Family Function
两个参数的Beta分布的家庭功能
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimation of the shape parameters of the two-parameter beta distribution.
两个参数的beta分布的形状参数的估计。
用法----------Usage----------
beta.ab(lshape1 = "loge", lshape2 = "loge",
eshape1 = list(), eshape2 = list(),
i1 = NULL, i2 = NULL, trim = 0.05,
A = 0, B = 1, parallel = FALSE, zero = NULL)
参数----------Arguments----------
参数：lshape1, lshape2
Parameter link functions applied to the two shape parameters. See Links for more choices. The log link (defaults) ensures that the parameters are positive.
参数链接功能施加到两个形状参数。见Links更多的选择。log链接（默认值）的参数是积极的。
参数：eshape1, eshape2
List. Extra argument for the links. 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：i1, i2
Initial value for the first and second shape parameters respectively. A NULL value means it is obtained in the initialize slot.
在第一和第二形状参数分别初始值。 ANULL这个值意味着它获得initialize插槽。
参数：trim
An argument which is fed into mean(); it is the fraction (0 to 0.5) of observations to be trimmed from each end of the response y before the mean is computed. This is used when computing initial values, and guards against outliers.
一个参数，该参数被送入mean();它是从每端的响应要修剪的分数（0至0.5）的观测y前的平均值计算。这是用来计算初始值，防离群。
参数：A, B
Lower and upper limits of the distribution. The defaults correspond to the standard beta distribution where the response lies between 0 and 1.
的上限和下限的分布。默认值对应的标准beta分布的响应是0和1之间。
参数：parallel, zero
See CommonVGAMffArguments for more information.
见CommonVGAMffArguments更多信息。
Details
详细信息----------Details----------
The two-parameter beta distribution is given by f(y) =
这两个参数的beta分布由f(y) =
for A < y < B, and Beta(.,.) is the beta function (see beta). The shape parameters are positive, and here, the limits A and B are known. The mean of Y is E(Y) = A + (B-A) * shape1 / (shape1 + shape2), and these are the fitted values of the object.
A < y < B和Beta(.,.)是β函数（见beta）。的形状参数是正的，并且在这里，限制A和B是已知的。 Y的平均E(Y) = A + (B-A) * shape1 / (shape1 + shape2)，而这些都是拟合值的对象。
For the standard beta distribution the variance of Y is shape1 * shape2 / ((1+shape1+shape2) * (shape1+shape2)^2). If σ^2= 1 / (1+shape1+shape2) then the variance of Y can be written mu*(1-mu)*sigma^2 where mu=shape1 / (shape1 + shape2) is the mean of Y.
对于标准beta分布的方差Y是 shape1 * shape2 / ((1+shape1+shape2) * (shape1+shape2)^2)。如果σ^2= 1 / (1+shape1+shape2)那么的方差Y可以书面mu*(1-mu)*sigma^2其中mu=shape1 / (shape1 + shape2)的平均Y。
Another parameterization of the beta distribution involving the mean and a precision parameter is implemented in betaff.
另一种参数化的β分布的均值和精度参数的实施betaff。
If A and B are unknown, then the VGAM family function beta4() can be used to estimate these too.
如果A和B是未知的，然后VGAM家庭功能beta4()可以用来估计这些。
值----------Value----------
An object of class "vglmff" (see vglmff-class). The object is used by modelling functions such as vglm, rrvglm and vgam.
类的一个对象"vglmff"（见vglmff-class）。该对象被用于建模功能，如vglm，rrvglm和vgam。
注意----------Note----------
The response must have values in the interval (A, B). VGAM 0.7-4 and prior called this function betaff.
的反应必须有值的时间间隔（A，B）。 VGAM0.7-4和之前调用此函数betaff。
（作者）----------Author(s)----------
Thomas W. Yee
参考文献----------References----------
Chapter 25 of: Continuous Univariate Distributions, 2nd edition, Volume 2, New York: Wiley.
Handbook of Beta Distribution and Its Applications, New York: Marcel Dekker.
http://www.stat.auckland.ac.nz/~yee contains further information and examples.
参见----------See Also----------
betaff, Beta, genbetaII, betaII, betabinomial.ab, betageometric, betaprime, rbetageom, rbetanorm, kumar, beta4.
betaff，Beta，genbetaII，betaII，betabinomial.ab，betageometric，betaprime，rbetageom，rbetanorm，kumar，beta4。
实例----------Examples----------
bdata = data.frame(y = rbeta(n = 1000, shape1 = exp(0), shape2 = exp(1)))
fit = vglm(y ~ 1, beta.ab(lshape1 = "identity", lshape2 = "identity"),
data = bdata, trace = TRUE, crit = "coef")
fit = vglm(y ~ 1, beta.ab, bdata, trace = TRUE, crit = "coef")
coef(fit, matrix = TRUE)
Coef(fit) # Useful for intercept-only models[仅截距模型]
bdata = transform(bdata, Y = 5 + 8 * y) # From 5 to 13, not 0 to 1[从5到13，而不是0到1的]
fit = vglm(Y ~ 1, beta.ab(A = 5, B = 13), bdata, trace = TRUE)
Coef(fit)
c(meanY = with(bdata, mean(Y)), head(fitted(fit),2))
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 2012-10-1 15:25:38
