betaff(VGAM)
betaff()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 mean and precision parameters of the beta distribution.
Beta分布的均值和精度参数的估计。
用法----------Usage----------
betaff(A = 0, B = 1,
lmu = if (A == 0 & B == 1) "logit" else "elogit", lphi = "loge",
emu = if (lmu == "elogit") list(min = A, max = B) else list(),
ephi = list(), imu = NULL, iphi = NULL, imethod = 1, zero = NULL)
参数----------Arguments----------
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：lmu, lphi
Link function for the mean and precision parameters. See below for more details. See Links for more choices.
Link功能的均值和精度的参数。请参阅下面的更多细节。见Links更多的选择。
参数：emu, ephi
List. Extra argument for the respective links. See earg in Links for general information.
列表。额外的参数，相应的链接。见earg中Links的一般信息。
参数：imu, iphi
Optional initial value for the mean and precision parameters respectively. A NULL value means a value is obtained in the initialize slot.
可选的初始值的平均值和精度参数。 ANULL值表示一个数值中获得initialize插槽。
参数：imethod, zero
See CommonVGAMffArguments for more information.
见CommonVGAMffArguments更多信息。
Details
详细信息----------Details----------
The two-parameter beta distribution can be written f(y) =
这两个参数的beta分布可以写成f(y) =
for A < y < B, and beta(.,.) is the beta function (see beta). The parameter mu1 satisfies mu1 = (mu - A) / (B-A) where mu is the mean of Y. That is, mu1 is the mean of of a standard beta distribution: E(Y) = A + (B-A)*mu1, and these are the fitted values of the object. Also, phi is positive and A < mu < B. Here, the limits A and B are known.
A < y < B和beta(.,.)是β函数（见beta）。参数mu1满足mu1 = (mu - A) / (B-A)mu的平均Y。也就是说，mu1是一个标准的Beta分布的平均值的：E(Y) = A + (B-A)*mu1，这些都是拟合值的对象。此外，phi是积极的，A < mu < B。在这里，限制A和B是众所周知的。
Another parameterization of the beta distribution involving the raw shape parameters is implemented in beta.ab.
涉及的原始形状参数的Beta分布的另一种参数化的实施beta.ab。
For general A and B, the variance of Y is (B-A)^2 * mu1 * (1-mu1) / (1+phi). Then phi can be interpreted as a precision parameter in the sense that, for fixed mu, the larger the value of phi, the smaller the variance of Y. Also, mu1=shape1/(shape1+shape2) and phi = shape1+shape2.
对于一般的A和B，方差Y是(B-A)^2 * mu1 * (1-mu1) / (1+phi)。然后phi可以被解释作为一个精确的参数在这个意义上，用于固定mu，的值越大phi，Y的方差越小。此外，mu1=shape1/(shape1+shape2)和phi = shape1+shape2。
Fisher scoring is implemented. If A and B are unknown then the VGAM family function beta4() can be used to estimate these too.
费舍尔得分的实施。如果A和B是未知的，那么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, and vgam.
类的一个对象"vglmff"（见vglmff-class）。该对象被用于建模功能，如vglm，vgam。
注意----------Note----------
The response must have values in the interval (A, B).
的反应必须有值的时间间隔（A，B）。
（作者）----------Author(s)----------
Thomas W. Yee
参考文献----------References----------
Beta regression for modelling rates and proportions. Journal of Applied Statistics, 31, 799–815.
http://www.stat.auckland.ac.nz/~yee contains further information and examples.
参见----------See Also----------
beta.ab, Beta, genbetaII, betaII, betabinomial.ab, betageometric, betaprime, rbetageom, rbetanorm, kumar, beta4, elogit.
beta.ab，Beta，genbetaII，betaII，betabinomial.ab，betageometric，betaprime，rbetageom，rbetanorm，kumar，beta4，elogit。
实例----------Examples----------
bdata = data.frame(y = rbeta(nn <- 1000, shape1 = exp(0), shape2 = exp(1)))
fit = vglm(y ~ 1, betaff, bdata, trace = TRUE)
coef(fit, matrix = TRUE)
Coef(fit) # Useful for intercept-only models[仅截距模型]
# General A and B, and with a covariate[一般A和B，并与协变量]
bdata = transform(bdata, x2 = runif(nn))
bdata = transform(bdata, mu = logit(0.5 - x2, inverse = TRUE),
prec = exp(3 + x2)) # prec == phi[PREC ==披]
bdata = transform(bdata, shape2 = prec * (1-mu),
shape1 = mu * prec)
bdata = transform(bdata, y = rbeta(nn, shape1 = shape1, shape2 = shape2))
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 ~ x2, betaff(A = 5, B = 13), bdata, trace = TRUE)
coef(fit, matrix = TRUE)
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 15:26:05
