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R语言 VGAM包 weibull()函数中文帮助文档(中英文对照)

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发表于 2012-10-1 15:58:06 | 显示全部楼层 |阅读模式
weibull(VGAM)
weibull()所属R语言包:VGAM

                                         Weibull Distribution Family Function
                                         Weibull分布的家庭功能

                                         译者:生物统计家园网 机器人LoveR

描述----------Description----------

Maximum likelihood estimation of the 2-parameter Weibull distribution. No observations should be censored.
2参数Weibull分布的极大似然估计。任何意见应被审查。


用法----------Usage----------


weibull(lshape = "loge", lscale = "loge",
        eshape = list(), escale = list(),
        ishape = NULL,   iscale = NULL, nrfs = 1, imethod = 1, zero = 2)



参数----------Arguments----------

参数:lshape, lscale
Parameter link functions applied to the  (positive) shape parameter (called a below) and (positive) scale parameter (called b below). See Links for more choices.  
施加到参数链接功能的(正)形状参数(叫做a以下)和(正)尺度参数(叫做b下文)。见Links更多的选择。


参数:eshape, escale
Extra argument for the respective links. See earg in Links for general information.  
额外的参数,相应的链接。见earg中Links的一般信息。


参数:ishape, iscale
Optional initial values for the shape and scale parameters.  
可选的形状和尺度参数的初始值。


参数:nrfs
Currently this argument is ignored. Numeric, of length one, with value in [0,1]. Weighting factor between Newton-Raphson and Fisher scoring. The value 0 means pure Newton-Raphson, while 1 means pure Fisher scoring. The default value uses a mixture of the two algorithms, and retaining positive-definite working weights.  
目前,该参数将被忽略。数字,长度为一,与价值在[0,1]。牛顿 - 拉夫逊和费舍尔得分的权重因子。值为0表示纯牛顿 - 拉夫逊,而并非单纯的费舍尔得分。默认值两种算法混合使用,并保留正定工作的权重。


参数:imethod
Initialization method used if there are censored observations. Currently only the values 1 and 2 are allowed.  
初始化方法使用,如果有被审查的意见。目前,只有被允许的值1和2。


参数:zero
An integer specifying which linear/additive predictor is to be modelled as an intercept only.  The value must be from the set {1,2}, which correspond to the shape and scale parameters respectively. Setting zero = NULL means none of them.  
一个整数,指定线性/添加剂的预测中是被定义成仅截距。该值必须是从集合{1,2},分别对应的形状和尺度参数。设置zero = NULL是指他们没有。


Details

详细信息----------Details----------

The Weibull density for a response Y is
威布尔密度为响应Y

for a > 0, b > 0, y > 0. The cumulative distribution function is
a > 0,b > 0,y > 0。累积分布函数是

The mean of Y is b * gamma(1+ 1/a) (returned as the fitted values), and the mode is at b * (1- 1/a)^(1/a) when a>1. The density is unbounded for a<1. The kth moment about the origin is E(Y^k) = b^k * gamma(1+ k/a). The hazard function is a * t^(a-1) / b^a.
的平均Y是b * gamma(1+ 1/a)(返回的拟合值),模式是在b * (1- 1/a)^(1/a)a>1。密度是无限的a<1。 k阶矩的起源是E(Y^k) = b^k * gamma(1+ k/a)。风险函数是a * t^(a-1) / b^a。

This VGAM family function currently does not handle  censored data. Fisher scoring is used to estimate the two parameters. Although the Fisher information matrices used here are valid in all regions of the parameter space, the regularity conditions for maximum likelihood estimation are satisfied only if a>2 (according to Kleiber and Kotz (2003)). If this is violated then a warning message is issued. One can enforce a>2 by choosing lshape = "logoff" and eshape = list(offset = -2).
目前这VGAM家庭功能不处理删失数据。 Fisher评分是用来估计的两个参数。虽然这里使用的是Fisher信息矩阵有效的参数空间的所有区域,最大似然估计的规律性条件感到满意,只有a>2(根据克莱伯和科茨(2003))。如果这是违反然后发出一条警告消息。一个可以执行a>2的选择lshape = "logoff"和eshape = list(offset = -2)。


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。


警告----------Warning----------

This function is under development to handle other censoring situations. The version of this function which will handle censored data will be called cenweibull(). It is currently being written and will use SurvS4 as input.  It should be released in later versions of VGAM.
此功能正在开发中,处理其他的审查情况。此功能将处理删失数据的版本将被称为cenweibull()。这是目前正在编写,并使用SurvS4作为输入。它应该被释放的VGAM在以后的版本。

If the shape parameter is less than two then misleading inference may result, e.g., in the summary and vcov of the object.
如果形状参数小于2,那么误导性的推断,可能会导致,例如,在summary和vcov的对象。


注意----------Note----------

Successful convergence depends on having reasonably good initial values. If the initial values chosen by this function are not good, make use the two initial value arguments.
成功的收敛依赖于具有相当不错的初始值。如果通过此功能选择的初始值并不好,利用两个初始值参数。

The Weibull distribution is often an alternative to the lognormal distribution.  The inverse Weibull distribution, which is that of 1/Y where Y has a Weibull(a,b) distribution, is known as the log-Gompertz distribution.
威布尔分布往往是对数正态分布的替代方法。逆Weibull分布,这是对1/Y其中Y威布尔(a,b)分布,被称为logGompertz分布。

There are problems implementing the three-parameter Weibull distribution. These are because the classical regularity conditions for the asymptotic properties of the MLEs are not satisfied because the support of the distribution depends on one of the parameters.
有实施的三参数Weibull分布的问题。这些是因为古典规律性条件的极大似然估计的渐近性质不满意,因为支持的分布取决于的参数中的一个。


(作者)----------Author(s)----------


T. W. Yee



参考文献----------References----------

Statistical Size Distributions in Economics and Actuarial Sciences, Hoboken, NJ, USA: Wiley-Interscience.
Continuous Univariate Distributions, 2nd edition, Volume 1, New York: Wiley.
The Weibull Distribution: A Handbook. Boca Raton, FL, USA: CRC Press.
On the comparison of Fisher information of the Weibull and GE distributions, Journal of Statistical Planning and Inference, 136, 3130&ndash;3144.
Maximum likelihood estimation in a class of nonregular cases. Biometrika, 72, 67&ndash;90.
A comparison of maximum likelihood and Bayesian estimators for the three-parameter Weibull distribution. Applied Statistics, 36, 358&ndash;369.

参见----------See Also----------

dweibull, gev, lognormal, expexp.
dweibull,gev,lognormal,expexp。


实例----------Examples----------


# Complete data[完整的数据]
wdata = data.frame(x2 = runif(nn <- 1000))
wdata = transform(wdata, y = rweibull(nn, shape = exp(1 + x2), scale = exp(-2)))
fit = vglm(y ~ x2, weibull, wdata, trace = TRUE)
coef(fit, matrix = TRUE)
vcov(fit)
summary(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。
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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