expexp(VGAM)
expexp()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 maximum likelihood estimation.
估计这两个参数的幂指数分布的最大似然估计。
用法----------Usage----------
expexp(lshape = "loge", lscale = "loge", eshape = list(), escale = list(),
ishape = 1.1, iscale = NULL, tolerance = 1.0e-6, zero = NULL)
参数----------Arguments----------
参数：lshape, lscale
Parameter link functions for the shape and scale parameters. See Links for more choices. The defaults ensure both parameters are positive.
参数的链接功能，shape和scale参数。见Links更多的选择。默认确保这两个参数是积极的。
参数：eshape, escale
List. Extra argument for each of the links. See earg in Links for general information.
列表。每个环节的额外参数。见earg中Links的一般信息。
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。
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：tolerance
Numeric. Small positive value for testing whether values are close enough to 1 and 2.
数字。测试值是否足够接近1和2的小的正值。
参数：zero
An integer-valued vector specifying which linear/additive predictors are modelled as intercepts only. The default is none of them. If used, choose one value from the set {1,2}.
指定一个整数值向量线性/添加剂的预测模型仅作为拦截。在默认情况下是没有。如果使用，选择一个值的集合{1,2}。
Details
详细信息----------Details----------
The exponentiated exponential distribution is an alternative to the Weibull and the gamma distributions. The formula for the density is
幂指数分布的Weibull分布和伽玛分布的替代。密度的公式是
where y>0, shape>0 and scale>0. The mean of Y is (psi(shape+1)-psi(1))/scale (returned as the fitted values) where psi is the digamma function. The variance of Y is (psi'(1)-psi'(shape+1))/ scale^2 where psi' is the trigamma function.
y>0，shape>0和scale>0。的平均Y是(psi(shape+1)-psi(1))/scale（返回的拟合值）psi是在digamma的功能。 Y的方差是(psi'(1)-psi'(shape+1))/ scale^2psi'是在trigamma的功能。
This distribution has been called the two-parameter generalized exponential distribution by Gupta and Kundu (2006). A special case of the exponentiated exponential distribution: shape=1 is the exponential distribution.
这个分布被称为双参数广义指数分布的古普塔和昆都（2006）。幂指数分布的一种特殊情况：shape=1是指数分布。
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 ----------
Practical experience shows that reasonably good initial values really helps. In particular, try setting different values for the ishape argument if numerical problems are encountered or failure to convergence occurs. Even if convergence occurs try perturbing the initial value to make sure the global solution is obtained and not a local solution. The algorithm may fail if the estimate of the shape parameter is too close to unity.
实践经验表明，相当不错的初始值确实有帮助。特别是，ishape参数设置不同的值，如果数值在遇到问题或故障收敛发生。即使收敛发生扰动的初始值，以确保获得全球性的解决方案，而不是一个本地的解决方案。该算法可能会失败，如果形状参数的估计是过于接近统一。
注意----------Note----------
Fisher scoring is used, however, convergence is usually very slow. This is a good sign that there is a bug, but I have yet to check that the expected information is correct. Also, I have yet to implement Type-I right censored data using the results of Gupta and Kundu (2006).
然而，使用Fisher评分，收敛通常是很慢的。这是一个很好的迹象，表明有是一个错误，但我还没有检查，预期的信息是正确的。此外，我还没有实现-I型右删失数据使用Gupta和昆都（2006）的结果。
Another algorithm for fitting this model is implemented in expexp1.
另一种算法拟合该模型中实现expexp1。
（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.
On the comparison of Fisher information of the Weibull and GE distributions, Journal of Statistical Planning and Inference, 136, 3130–3144.
参见----------See Also----------
expexp1, gamma2.ab, weibull, CommonVGAMffArguments.
expexp1，gamma2.ab，weibull，CommonVGAMffArguments。
实例----------Examples----------
# A special case: exponential data[特殊情况：指数数据]
y = rexp(n <- 1000)
fit = vglm(y ~ 1, fam = expexp, trace = TRUE, maxit = 99)
coef(fit, matrix=TRUE)
Coef(fit)
# Ball bearings data (number of million revolutions before failure)[球轴承数据（故障前的万转数）]
bbearings = 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(bbearings ~ 1, fam = expexp(iscale = 0.05, ish = 5),
trace = TRUE, maxit = 300)
coef(fit, matrix = TRUE)
Coef(fit) # Authors get c(shape=5.2589, scale=0.0314)[得到C（形状= 5.2589，规模= 0.0314）]
logLik(fit) # Authors get -112.9763[作者得到-112.9763]
# Failure times of the airconditioning system of an airplane[飞机上的空调系统的故障时间]
acplane = 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(acplane ~ 1, fam = expexp(ishape = 0.8, isc = 0.15),
trace = TRUE, maxit = 99)
coef(fit, matrix = TRUE)
Coef(fit) # Authors get c(shape=0.8130, scale=0.0145)[得到C（形状= 0.8130，规模= 0.0145）]
logLik(fit) # Authors get log-lik -152.264[作者log力-152.264]
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。
|
发表于 2012-10-1 15:33:09
