gamma.shape.glm(MASS)
gamma.shape.glm()所属R语言包：MASS
Estimate the Shape Parameter of the Gamma Distribution in a GLM Fit
Gamma分布的形状参数估计中的GLM飞度
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Find the maximum likelihood estimate of the shape parameter of the gamma distribution after fitting a Gamma generalized linear model.
Gamma广义线性模型拟合后发现伽玛分布的形状参数的最大似然估计。
用法----------Usage----------
## S3 method for class 'glm'
gamma.shape(object, it.lim = 10,
eps.max = .Machine$double.eps^0.25, verbose = FALSE, ...)
参数----------Arguments----------
参数：object
Fitted model object from a Gamma family or quasi family with variance = "mu^2".
Gamma家庭quasivariance = "mu^2"家庭的拟合模型对象。
参数：it.lim
Upper limit on the number of iterations.
迭代次数的上限。
参数：eps.max
Maximum discrepancy between approximations for the iteration process to continue.
继续迭代过程逼近之间的最大差异。
参数：verbose
If TRUE, causes successive iterations to be printed out. The initial estimate is taken from the deviance.
如果TRUE，导致连续迭代被打印出来。初步估计是从越轨行为。
参数：...
further arguments passed to or from other methods. </table>
通过进一步的论据或其他方法。 </ TABLE>
Details
详情----------Details----------
A glm fit for a Gamma family correctly calculates the maximum likelihood estimate of the mean parameters but provides only a crude estimate of the dispersion parameter. This function takes the results of the glm fit and solves the maximum likelihood equation for the reciprocal of the dispersion parameter, which is usually called the shape (or exponent) parameter.
一个GLM合适的伽玛系列正确计算平均参数的最大似然估计，但只提供了分散参数的粗略估计。此功能的GLM合适的结果，并解决了最大似然方程的色散参数，它通常被称为形状（或指数）的参数的倒数。
值----------Value----------
List of two components
两部分组成名单
参数：alpha
the maximum likelihood estimate
最大似然估计
参数：SE
the approximate standard error, the square-root of the reciprocal of the observed information. </table>
近似的标准误差，对观测资料的倒数平方根。 </ TABLE>
参考文献----------References----------
Modern Applied Statistics with S. Fourth edition. Springer.
参见----------See Also----------
gamma.dispersion
gamma.dispersion
举例----------Examples----------
clotting <- data.frame(
u = c(5,10,15,20,30,40,60,80,100),
lot1 = c(118,58,42,35,27,25,21,19,18),
lot2 = c(69,35,26,21,18,16,13,12,12))
clot1 <- glm(lot1 ~ log(u), data = clotting, family = Gamma)
gamma.shape(clot1)
gm <- glm(Days + 0.1 ~ Age*Eth*Sex*Lrn,
quasi(link=log, variance="mu^2"), quine,
start = c(3, rep(0,31)))
gamma.shape(gm, verbose = TRUE)
summary(gm, dispersion = gamma.dispersion(gm)) # better summary[更好地总结]
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。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-2-16 21:12:42
