expexp1(VGAM)
expexp1()所属R语言包：VGAM
Exponentiated Exponential Distribution
幂指数分布
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimates the two parameters of the exponentiated exponential distribution by maximizing a profile (concentrated) likelihood.
估计这两个参数的幂指数分布，最大化配置文件（浓缩型）的可能性。
用法----------Usage----------
expexp1(lscale = "loge", escale = list(), iscale = NULL, ishape = 1)
参数----------Arguments----------
参数：lscale
Parameter link function for the (positive) scale parameter. See Links for more choices.
的（正）scale参数参数链接功能。见Links更多的选择。
参数：escale
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：iscale
Initial value for the scale parameter. By default, an initial value is chosen internally using ishape.
的初始值scale参数。默认情况下，初始值是内部选择使用ishape。
参数：ishape
Initial value for the shape parameter. If convergence fails try setting a different value for this argument.
的初始值shape参数。如果收敛失败的话请尝试把此参数设置不同的值。
Details
详细信息----------Details----------
See expexp for details about the exponentiated exponential distribution. This family function uses a different algorithm for fitting the model. Given scale, the MLE of shape can easily be solved in terms of scale. This family function maximizes a profile (concentrated) likelihood with respect to scale. Newton-Raphson is used, which compares with Fisher scoring with expexp.
见expexp的幂指数分布的详细信息。这间家庭功能使用不同的算法拟合模型。鉴于scale，MLEshape可以很容易地得到解决在scale方面。这间家庭功能最大化的配置文件（浓缩）的可能性就到scale。牛顿 - 拉夫逊，与费舍尔得分expexp。
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。
警告----------Warning ----------
The standard errors produced by a summary of the model may be wrong.
一个summary的模型所产生的标准误差可能是错误的。
注意----------Note----------
This family function works only for intercept-only models, i.e., y ~ 1 where y is the response.
这的家庭功能仅适用于仅截距模型，即，y ~ 1y是响应。
The estimate of shape is attached to the misc slot of the object, which is a list and contains the component shape.
估计shape安装的misc插槽的对象，这是一个列表，并包含该组件shape。
As Newton-Raphson is used, the working weights are sometimes negative, and some adjustment is made to these to make them positive.
正如使用Newton-Raphson法，工作的权重是有时是消极的，和一些调整这些并使它们积极。
Like expexp, good initial values are needed. Convergence may be slow.
类似expexp，良好的初始值是必要的。收敛速度可能很慢。
（作者）----------Author(s)----------
T. W. Yee
参考文献----------References----------
Exponentiated exponential family: an alternative to gamma and Weibull distributions, Biometrical Journal, 43, 117–130.
参见----------See Also----------
expexp, CommonVGAMffArguments.
expexp，CommonVGAMffArguments。
实例----------Examples----------
# Ball bearings data (number of million revolutions before failure)[球轴承数据（故障前的万转数）]
bbearings = data.frame(y = c(17.88, 28.92, 33.00, 41.52, 42.12, 45.60,
48.80, 51.84, 51.96, 54.12, 55.56, 67.80, 68.64, 68.64,
68.88, 84.12, 93.12, 98.64, 105.12, 105.84, 127.92,
128.04, 173.40))
fit = vglm(y ~ 1, expexp1(ishape = 4), bbearings, trace = TRUE,
maxit = 50, checkwz = FALSE)
coef(fit, matrix = TRUE)
Coef(fit) # Authors get c(0.0314, 5.2589) with log-lik -112.9763[得到C（0.0314，5.2589），数力-112.9763]
fit@misc$shape # Estimate of shape[估计形状]
logLik(fit)
# Failure times of the airconditioning system of an airplane[飞机上的空调系统的故障时间]
acplane = data.frame(y = c(23, 261, 87, 7, 120, 14, 62, 47,
225, 71, 246, 21, 42, 20, 5, 12, 120, 11, 3, 14,
71, 11, 14, 11, 16, 90, 1, 16, 52, 95))
fit = vglm(y ~ 1, expexp1(ishape = 0.8), acplane, trace = TRUE,
maxit = 50, checkwz = FALSE)
coef(fit, matrix = TRUE)
Coef(fit) # Authors get c(0.0145, 0.8130) with log-lik -152.264[得到C（0.0145，0.8130），数力-152.264]
fit@misc$shape # Estimate of shape[估计形状]
logLik(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 15:33:15
