bivgamma.mckay(VGAM)
bivgamma.mckay()所属R语言包：VGAM
Bivariate Gamma: McKay's Distribution
二元伽玛：McKay的分布
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimate the three parameters of McKay's bivariate gamma distribution by maximum likelihood estimation.
McKay的二元伽玛分布的三个参数的估计，最大似然估计。
用法----------Usage----------
bivgamma.mckay(lscale = "loge", lshape1 = "loge", lshape2 = "loge",
iscale = NULL, ishape1 = NULL, ishape2 = NULL,
imethod=1, zero = 1)
参数----------Arguments----------
参数：lscale, lshape1, lshape2
Link functions applied to the (positive) parameters a, p and q respectively. See Links for more choices.
链接功能的参数（正）a，p和q分别施加。见Links更多的选择。
参数：iscale, ishape1, ishape2
Optional initial values for a, p and q respectively. The default is to compute them internally.
可选初始值a，p和q分别。默认值是内部计算。
参数：imethod, zero
See CommonVGAMffArguments.
见CommonVGAMffArguments。
Details
详细信息----------Details----------
One of the earliest forms of the bivariate gamma distribution has a joint probability density function given by
的二元伽玛分布的早期形式之一的联合概率密度函数
for a > 0, p > 0, q > 0 and 0<y1<y2 (Mckay, 1934). Here, gamma is the gamma function, as in gamma. By default, the linear/additive predictors are eta1=log(a), eta2=log(p), eta3=log(q).
a > 0，p > 0，q > 0和0<y1<y2（麦凯，1934年）。在这里，gamma是伽玛函数，如gamma。默认情况下，线性/对添加剂的预测是eta1=log(a)，eta2=log(p)，eta3=log(q)。
The marginal distributions are gamma, with shape parameters p and p+q respectively, but they have a common scale parameter a. Pearson's product-moment correlation coefficient of y1 and y2 is sqrt(p/(p+q)). This distribution is also known as the bivariate Pearson type III distribution. Also, Y2 - y1, conditional on Y1=y1, has a gamma distribution with shape parameter q.
的边缘分布是伽玛，带形状参数的p和p+q分别，但他们都有一个共同的尺度参数a。皮尔逊积矩相关系数的y1和y2是sqrt(p/(p+q))。这种分布也被称为二元皮尔逊III型分布。此外，Y2 - y1，条件上Y1=y1，具有伽玛分布形状参数q。
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和vgam。
注意----------Note----------
The response must be a two column matrix where the first column is y1 and the second y2. It is necessary that each element of the vectors y1 and y2-y1 be positive. Currently, the fitted value is a matrix with two columns; the first column has values ap for the marginal mean of y1, while the second column has values a(p+q) for the marginal mean of y2 (all evaluated at the final iteration).
响应必须是一个两列的矩阵，其中第一列是y1和第二y2的。这是必要的，向量的每个元素y1和y2-y1是正面。目前，拟合的值是一个矩阵具有两列的第一列具有值apy1边际平均，而第二列具有值a(p+q)为边际平均值 y2（所有在最后一次迭代计算）。
（作者）----------Author(s)----------
T. W. Yee
参考文献----------References----------
Sampling from batches. Journal of the Royal Statistical Society—Supplement, 1, 207–216.
Continuous Multivariate Distributions Volume 1: Models and Applications, 2nd edition, New York: Wiley.
Continuous Bivariate Distributions, 2nd edition. New York: Springer.
参见----------See Also----------
gamma2.
gamma2。
实例----------Examples----------
shape1 = exp(1); shape2 = exp(2); scalepar = exp(3)
mdata = data.frame(y1 = rgamma(nn <- 1000, shape=shape1, scale=scalepar))
mdata = transform(mdata, zedd = rgamma(nn, shape=shape2, scale=scalepar))
mdata = transform(mdata, y2 = y1 + zedd) # Z is defined as Y2-y1|Y1=y1[Z定义为Y2-Y1 | Y1 = Y1]
fit = vglm(cbind(y1, y2) ~ 1, bivgamma.mckay, mdata, trace = TRUE)
coef(fit, matrix = TRUE)
Coef(fit)
vcov(fit)
colMeans(depvar(fit)) # Check moments[检查的瞬间]
head(fitted(fit), 1)
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 15:27:28
