R语言 VineCopula包 BiCopGofKendall()函数中文帮助文档(中英文对照)

loveR · 发表于 2012-10-1 16:09:15

BiCopGofKendall(VineCopula)
BiCopGofKendall()所属R语言包：VineCopula

 Goodness-of-fit test based on Kendall's process for bivariate copula data
 Kendall的过程中二元Copula的数据的的基础上的拟合优度测试

 译者：生物统计家园网机器人LoveR

描述----------Description----------

This function performs the goodness-of-fit test based on Kendall's process for bivariate copula data. It computes the Cramer-von Mises and Kolmogorov-Smirnov test statistics, respectively, as well as the according p-values using bootstrapping.
这个函数执行的善良的拟合优度检验，Kendall的过程中二元Copula的数据的的基础上的。它计算的Cramer-von Mises和Kolmogorov-Smirnov检验统计，以及相应的p值使用自举。

用法----------Usage----------

BiCopGofKendall(u1, u2, family, B=100, level=0.05)

参数----------Arguments----------

参数：u1,u2
Data vectors of equal length with values in [0,1].
数据向量长度相等的值在[0,1]。

参数：family
An integer defining the bivariate copula family for which the test is performed: 1 = Gaussian copula 2 = Student t copula (t-copula) 3 = Clayton copula 4 = Gumbel copula 5 = Frank copula 6 = Joe copula 7 = BB1 copula 8 = BB6 copula 9 = BB7 copula 10 = BB8 copula 13 = rotated Clayton copula (180 degrees; “survival Clayton”) 14 = rotated Gumbel copula (180 degrees; “survival Gumbel”) 16 = rotated Joe copula (180 degrees; “survival Joe”) 17 = rotated BB1 copula (180 degrees; “survival BB1”) 18 = rotated BB6 copula (180 degrees; “survival BB6”) 19 = rotated BB7 copula (180 degrees; “survival BB7”) 20 = rotated BB8 copula (180 degrees; “survival BB8”) 23 = rotated Clayton copula (90 degrees) 24 = rotated Gumbel copula (90 degrees) 26 = rotated Joe copula (90 degrees) 27 = rotated BB1 copula (90 degrees) 28 = rotated BB6 copula (90 degrees) 29 = rotated BB7 copula (90 degrees) 30 = rotated BB8 copula (90 degrees) 33 = rotated Clayton copula (270 degrees) 34 = rotated Gumbel copula (270 degrees) 36 = rotated Joe copula (270 degrees) 37 = rotated BB1 copula (270 degrees) 38 = rotated BB6 copula (270 degrees) 39 = rotated BB7 copula (270 degrees) 40 = rotated BB8 copula (270 degrees)
一个整数，测试定义二元Copula的家庭的“： 1=高斯系词参考的2学生t Copula函数（T-Copula函数）参考3是克莱顿系词参考4=冈贝尔系词参考5=弗兰克·系词参考6=乔系词参考7= BB1 Copula函数参考8= BB6 Copula的参考9= BB7系词参考10= BB8系词参考13=旋转（180度“生存克莱顿”克莱顿系词;)参考14=旋转（180度“生存冈贝尔”）Gumbel分布Copula的参考16=旋转乔系词（180度;“生存乔”） 17=旋转BB1 Copula函数（180度;“BB1生存”）参考18=旋转BB6 Copula函数（180度“生存BB6”）参考19=旋转BB7系词（180度“生存BB7”）参考20=旋转BB8系词（180度“生存BB8”）参考23 =旋转克莱顿系词（90度）参考24=系词（90度）旋转冈贝尔参考26=旋转乔系词（90度）参考27=旋转BB1 Copula函数（90度） >28=旋转（90度）BB6 Copula的参考29=旋转BB7系词（90度）参考30=旋转BB8系词（90度）参考 33 =系词（270度）旋转克莱顿参考34=系词（270度）旋转冈贝尔参考36=旋转乔系词（270度）参考<X >=旋转BB1 Copula函数（270度）参考37=旋转（270度）BB6 Copula的参考38=旋转BB7系词（270度）参考39 =旋转BB8系词（270度）

参数：B
Integer; number of bootstrap samples (default: B = 100). For B = 0 only the the test statistics are returned. WARNING: If B is chosen too large, computations will take very long.
整数的bootstrap样本数量（默认：B = 100）。对于B = 0返回的检验统计量的。警告：如果B选择过大，计算将需要很长时间。

参数：level
Numeric; significance level of the goodness-of-fit test (default: level = 0.05).
数字;善良的拟合优度检验的显着性水平（默认：level = 0.05）。

Details

详细信息----------Details----------

This copula goodness-of-fit test is based on Kendall's process as investigated by Genest and Rivest (1993) and Wang and Wells (2000). For rotated copulas the input arguments are transformed and the goodness-of-fit procedure for the corresponding non-rotated copula is used.
这系词善良的拟合优度检验的基础上肯德尔Genest和Rivest（1993）的王和韦尔斯（2000）调查的过程中。对于旋转Copula函数的输入参数被转换，并使用相应的非旋转系词善良的配合程序。

值----------Value----------

参数：p.value.CvM
P-value of the goodness-of-fit test using the Cramer-von Mises statistic (if B > 0).
P-值的善良的拟合优度检验，使用克莱姆·冯·米塞斯的统计 （如果B > 0）。

参数：p.value.KS
P-value of the goodness-of-fit test using the Kolmogorov-Smirnov statistic (if B > 0).
使用的Kolmogorov-斯米尔诺夫统计 的的的善良的拟合优度检验的P值（如果B > 0“）。

参数：statistic.CvM
The observed Cramer-von Mises test statistic.
所观察到的Cramer-von Mises的检验统计量。

参数：statistic.KS
The observed Kolmogorov-Smirnov test statistic.
所观察到的Kolmogorov-Smirnov检验统计量。

（作者）----------Author(s)----------

Jiying Luo, Eike Brechmann

参考文献----------References----------

Statistical inference procedures for bivariate Archimedean copulas. Journal of the American Statistical Association, 88 (423), 1034-1043.
Stepwise estimation of D-vines with arbitrary specified copula pairs and EDA Tools. Diploma thesis, Technische Universitaet Muenchen. http://mediatum.ub.tum.de/?id=1079291.
Model selection and semiparametric inference for bivariate failure-time data. Journal of the American Statistical Association, 95 (449), 62-72.

参见----------See Also----------

BiCopIndTest, BiCopSelect, BiCopVuongClarke, BiCopKPlot, BiCopLambda
BiCopIndTest，BiCopSelect，BiCopVuongClarke，BiCopKPlot，BiCopLambda

实例----------Examples----------

# sample from a Gaussian copula[样品从高斯系词]
par1 = 3
fam1 = 3
dat1 = BiCopSim(500,fam1,par1)

# perform the goodness-of-fit test for the true copula[真正的Copula函数执行善良的拟合优度检验]
gof = BiCopGofKendall(dat1[,1],dat1[,2],fam1)
gof$p.value.CvM
gof$p.value.KS

# perform the goodness-of-fit test for the Frank copula[执行善良的拟合优度检验的弗兰克·系词]
gof = BiCopGofKendall(dat1[,1],dat1[,2],5)
gof$p.value.CvM
gof$p.value.KS

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。

账号		自动登录	找回密码
密码			注册