BiCopVuongClarke(VineCopula)
BiCopVuongClarke()所属R语言包：VineCopula
Scoring goodness-of-fit test based on Vuong and Clarke tests for bivariate copula data
评分善良的拟合优度检验的基础上Vuong和克拉克为二元Copula的数据的测试
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Based on the Vuong and Clarke tests this function computes a goodness-of-fit score for each bivariate copula family under consideration. For each possible pair of copula families the Vuong and the Clarke tests decides which of the two families fits the given data best and assigns a score—pro or contra a copula family—according to this decision.
Vuong和克拉克测试的基础上，正在考虑这个函数计算一个善良的配合得分为每个二元Copula的家庭。对于每一个可能对Copula的家庭的的Vuong和克拉克测试确定的两个家庭中的哪一个最适合给定的数据和Copula的家庭根据本决定作分配分数亲或禁忌。
用法----------Usage----------
BiCopVuongClarke(u1, u2, familyset=NA,
correction=FALSE, 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：familyset
An integer vector of bivariate copula families under consideration, i.e., which are compared in the goodness-of-fit test. If familyset = NA (default), all possible families are compared. Possible families are: <br> 0 = independence copula <br> 1 = Gaussian copula <br> 2 = Student t copula (t-copula) <br> 3 = Clayton copula <br> 4 = Gumbel copula <br> 5 = Frank copula <br> 6 = Joe copula <br> 7 = BB1 copula <br> 8 = BB6 copula <br> 9 = BB7 copula <br> 10 = BB8 copula <br> 13 = rotated Clayton copula (180 degrees; “survival Clayton”) <br> 14 = rotated Gumbel copula (180 degrees; “survival Gumbel”) <br> 16 = rotated Joe copula (180 degrees; “survival Joe”) <br> 17 = rotated BB1 copula (180 degrees; “survival BB1”)<br> 18 = rotated BB6 copula (180 degrees; “survival BB6”)<br> 19 = rotated BB7 copula (180 degrees; “survival BB7”)<br> 20 = rotated BB8 copula (180 degrees; “survival BB8”)<br> 23 = rotated Clayton copula (90 degrees) <br> 24 = rotated Gumbel copula (90 degrees) <br> 26 = rotated Joe copula (90 degrees) <br> 27 = rotated BB1 copula (90 degrees) <br> 28 = rotated BB6 copula (90 degrees) <br> 29 = rotated BB7 copula (90 degrees) <br> 30 = rotated BB8 copula (90 degrees) <br> 33 = rotated Clayton copula (270 degrees) <br> 34 = rotated Gumbel copula (270 degrees) <br> 36 = rotated Joe copula (270 degrees) <br> 37 = rotated BB1 copula (270 degrees) <br> 38 = rotated BB6 copula (270 degrees) <br> 39 = rotated BB7 copula (270 degrees) <br> 40 = rotated BB8 copula (270 degrees)
一个整数向量二元Copula的家庭的考虑，即，这是比较善良的拟合优度检验。如果familyset = NA（默认），所有可能的家庭进行比较。可能的家庭是：<BR>0独立系词参考1=高斯系词参考的2学生t Copula函数（T-Copula函数）参考3 =克莱顿系词参考4= Gumbel分布Copula的参考5=弗兰克·系词参考6=乔系词参考7= BB1 Copula函数< >8= BB6 Copula的参考9= BB7系词参考10= BB8系词参考13 =旋转克莱顿系词（180度“生存克莱顿“）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“）参考17=旋转（180度;“BB1生存”）BB1 Copula的参考18=旋转BB6 Copula函数（180度“生存BB6”）参考19=旋转BB7 Copula的参考20=旋转BB8系词（180度（180度“生存BB7”），“生存BB8”）参考23=旋转克莱顿系词（90度）参考24=旋转冈贝尔系词（90度）参考26=旋转乔系词（90度）参考27=旋转BB1 Copula函数（90度）< BR> 28=旋转BB6 Copula函数（90度）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 =（270度）旋转乔系词参考 x> =旋转BB1 Copula函数（270度）参考37=旋转BB6 Copula函数（270度）参考38=旋转BB7系词（270度）参考39 =旋转BB8系词（270度）
参数：correction
Correction for the number of parameters. Possible choices: correction = FALSE (no correction; default), "Akaike" and "Schwarz".
校正参数的数目。可能的选择：correction = FALSE（无修正;默认值），"Akaike"和"Schwarz"。
参数：level
Numerical; significance level of the tests (default: level = 0.05).
数值显着性水平测试（默认：level = 0.05）。
Details
详细信息----------Details----------
The Vuong as well as the Clarke test compare two models against each other and based on their null hypothesis, allow for a statistically significant decision among the two models (see the documentations of RVineVuongTest and RVineClarkeTest for descriptions of the two tests). In the goodness-of-fit test proposed by Belgorodski (2010) this is used for bivariate copula selection. It compares a model 0 to all other possible models under consideration. If model 0 is favored over another model, a score of "+1" is assigned and similarly a score of "-1" if the other model is determined to be superior. No score is assigned, if the respective test cannot discriminate between two models. Both tests can be corrected for the numbers of parameters used in the copulas. Either no correction (correction = FALSE), the Akaike correction (correction = "Akaike") or the parsimonious Schwarz correction (correction = "Schwarz") can be used.
王街以及克拉克测试比较两个模型对对方的零假设的基础上，允许有统计学意义的决定，在两个模型中（见记载RVineVuongTest和RVineClarkeTest的说明，两个测试）。在善良的拟合优度检验的Belgorodski（2010）提出，这是用于二元Copula的选择。比较模式0下考虑的所有其他可能的模型。如果模型优于另一种模式，分数分配的“+1”和“-1”同样的分数，如果要优于其他模型。没有得分的分配，如果各自的测试不能区分两个模型。两个测试都可以被校正的Copula函数中使用的参数的数目。要么没有校正（correction = FALSE），校正的Akaike（correction = "Akaike"）或吝啬施瓦茨校正（correction = "Schwarz"）可以使用。
The models compared here are bivariate parametric copulas and we would like to determine which family fits the data better than the other families. E.g., if we would like to test the hypothesis that the bivariate Gaussian copula fits the data best, then we compare the Gaussian copula against all other copulas under consideration. In doing so, we investigate the null hypothesis "The Gaussian copula fits the data better than all other copulas under consideration", which corresponds to k-1 times the hypothesis "The Gaussian copula C_j fits the data better than copula C_i" for all i=1,...,k, i!=j, where k is the number of bivariate copula families under consideration (length of familyset). This procedure is done not only for one family but for all families under consideration, i.e., two scores, one based on the Vuong and one based on the Clarke test, are returned for each bivariate copula family. If used as a goodness-of-fit procedure, the family with the highest score should be selected.
机型相比，这里的二元参数Copula函数，我们想确定数据的吻合程度比其他家庭的家庭。例如，如果我们想测试的假设，即二元高斯Copula函数最适合的数据，那么我们比较了高斯系词正在考虑对所有其他Copula函数。在这样做时，我们调查的零假设“的高斯Copula函数拟合数据的考虑下比所有其他Copula函数”，它对应到k-1倍的假设，“高斯Copula的C_j适合的数据，比系词C_i“所有的”i=1,...,k, i!=j，其中k是多少二元Copula的家庭的代价（长度下familyset）。此过程不仅是一个家庭，而是为所有的家庭在考虑，即两个分数，一个基于Vuong和克拉克测试的基础上，将返回为每个二元Copula的家庭。如果作为一个善良的拟合过程中，得分最高的家庭，应选择。
For more and detailed information about the goodness-of-fit test see Belgorodski (2010).
善良的拟合优度检验更多的详细信息，请参阅Belgorodski（2010年）。
值----------Value----------
A matrix with Vuong test scores in the first and Clarke test scores in the second row. Column names correspond to bivariate copula families (see above).
王考试成绩第一和克拉克考试成绩排在第二的矩阵。列名对应的二元Copula的家庭（见上文）。
（作者）----------Author(s)----------
Ulf Schepsmeier, Eike Brechmann, Natalia Belgorodski
参考文献----------References----------
Selecting pair-copula families for regular vines with application to the multivariate analysis of European stock market indices Diploma thesis, Technische Universitaet Muenchen. http://mediatum.ub.tum.de/?id=1079284.
A Simple Distribution-Free Test for Nonnested Model Selection. Political Analysis, 15, 347-363.
Ratio tests for model selection and non-nested hypotheses. Econometrica 57 (2), 307-333.
参见----------See Also----------
BiCopGofKendall, RVineVuongTest, RVineClarkeTest, BiCopSelect
BiCopGofKendall，RVineVuongTest，RVineClarkeTest，BiCopSelect
实例----------Examples----------
# simulate from a t-copula[模拟从T-Copula函数]
dat = BiCopSim(500,2,0.7,5)
# apply the test for families 1-10[应用测试的家庭1-10]
vcgof = BiCopVuongClarke(dat[,1],dat[,2],familyset=c(1:10))
# display the Vuong test scores[显示Vuong的测试成绩]
vcgof[1,]
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。
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发表于 2012-10-1 16:10:43
