找回密码
 注册
查看: 438|回复: 0

R语言 VGAM包 logistic()函数中文帮助文档(中英文对照)

[复制链接]
发表于 2012-10-1 15:42:03 | 显示全部楼层 |阅读模式
logistic(VGAM)
logistic()所属R语言包:VGAM

                                         Logistic Distribution Family Function
                                         MF配送家庭功能

                                         译者:生物统计家园网 机器人LoveR

描述----------Description----------

Estimates the location and scale parameters of the logistic distribution by maximum likelihood estimation.
位置与刻度参数估计的最大似然估计的MF配送。


用法----------Usage----------


logistic1(llocation = "identity", elocation = list(),
          scale.arg = 1, imethod = 1)
logistic2(llocation = "identity", lscale = "loge",
          elocation = list(), escale = list(),
          ilocation = NULL, iscale = NULL, imethod = 1, zero = NULL)



参数----------Arguments----------

参数:llocation, lscale
Parameter link functions applied to the location parameter l and scale parameter s. See Links for more choices, and CommonVGAMffArguments for more information.  
参数链接功能的位置参数l和尺度参数s。见Links更多的选择,CommonVGAMffArguments的详细信息。


参数:elocation, 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:scale.arg
Known positive scale parameter (called s below).  
已知的正标参数(称为s下面)。


参数:ilocation, iscale
See CommonVGAMffArguments for more information.  
见CommonVGAMffArguments更多信息。


参数:imethod, zero
See CommonVGAMffArguments for more information.  
见CommonVGAMffArguments更多信息。


Details

详细信息----------Details----------

The two-parameter logistic distribution has a density that can be written as
两个参数的MF配送具有密度可以写为

where s > 0 is the scale parameter, and l is the location parameter. The response -Inf<y<Inf.  The mean of Y (which is the fitted value) is l and its variance is pi^2 s^2 / 3.
s > 0是尺度参数,并l是位置参数。的响应-Inf<y<Inf。的平均值Y(这是拟合值)是l和它的方差是pi^2 s^2 / 3。

A logistic distribution with scale = 0.65 (see dlogis) resembles dt with df = 7; see logistic1 and studentt.
一个MF配送scale = 0.65(见dlogis)类似dt与df = 7看logistic1和studentt。

logistic1 estimates the location parameter only while logistic2 estimates both parameters. By default, eta1 = l and eta2 = log(s) for logistic2.
logistic1估计位置参数,而logistic2估计这两个参数。默认情况下,eta1 = l和eta2 = log(s)logistic2。


值----------Value----------

An object of class "vglmff" (see vglmff-class). The object is used by modelling functions such as vglm, rrvglm and vgam.
类的一个对象"vglmff"(见vglmff-class)。该对象被用于建模功能,如vglm,rrvglm和vgam。


注意----------Note----------

Fisher scoring is used, and the Fisher information matrix is diagonal.
费舍尔得分时,和Fisher信息矩阵是对角。


(作者)----------Author(s)----------


T. W. Yee



参考文献----------References----------

Continuous Univariate Distributions, 2nd edition, Volume 1, New York: Wiley.  Chapter 15.
Statistical Distributions, New York: Wiley-Interscience, Third edition.
Extreme Value and Related Models with Applications in Engineering and Science, Hoboken, NJ, USA: Wiley-Interscience, p.130.
A note on Deriving the Information Matrix for a Logistic Distribution, The American Statistician, 40, 220&ndash;222.

参见----------See Also----------

rlogis, logit, cumulative, bilogistic4.
rlogis,logit,cumulative,bilogistic4。


----------Examples----------


# location unknown, scale known[位置未知,已知的规模]
ldat1 = data.frame(x = runif(nn <- 500))
ldat1 = transform(ldat1, y = rlogis(nn, loc = 1+5*x, scale = 4))
fit = vglm(y ~ x, logistic1(scale = 4), ldat1, trace = TRUE, crit = "c")
coef(fit, matrix = TRUE)

# Both location and scale unknown[未知的位置和规模]
ldat2 = data.frame(x = runif(nn <- 2000))
ldat2 = transform(ldat2, y = rlogis(nn, loc = 1+5*x, scale = exp(0+1*x)))
fit = vglm(y ~ x, logistic2, ldat2)
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。
回复

使用道具 举报

您需要登录后才可以回帖 登录 | 注册

本版积分规则

手机版|小黑屋|生物统计家园 网站价格

GMT+8, 2024-11-26 15:41 , Processed in 0.023139 second(s), 16 queries .

Powered by Discuz! X3.5

© 2001-2024 Discuz! Team.

快速回复 返回顶部 返回列表