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

loveR · 发表于 2012-10-1 16:12:24

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

 Sequential specification of R- and C-vine copula models
 顺序规格的R-和C-藤Copula函数模型

 译者：生物统计家园网机器人LoveR

描述----------Description----------

This function fits either an R- or a C-vine copula model to a d-dimensional copula data set. Tree structures are determined and appropriate pair-copula families are selected using BiCopSelect and estimated sequentially (forward selection of trees).
此功能适用R-或C-藤copula模型一个d维的的Copula的数据集。树结构的确定和选择适当对Copula的家庭，都使用BiCopSelect估计顺序（前向选择树）。

用法----------Usage----------

RVineStructureSelect(data, familyset=NA, type=0, selectioncrit="AIC",
 indeptest=FALSE, level=0.05, progress=FALSE)

参数----------Arguments----------

参数：data
An N x d data matrix (with uniform margins).
一个N×d数据矩阵（均匀的利润）。

参数：familyset
An integer vector of pair-copula families to select from (the independence copula MUST NOT be specified in this vector unless one wants to fit an independence vine!). The vector has to include at least one pair-copula family that allows for positive and one that allows for negative dependence. If familyset = NA (default), selection among all possible families is performed. Coding of pair-copula families: 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)
一个整数向量对Copula的家庭，选择（独立Copula函数，不得指定在此向量，除非要适应一个独立的藤！）。该向量具有包括至少一对的copula家庭，使正极和一个允许负相依。如果familyset = NA（默认），在所有可能的家庭进行选择。编码对Copula的家庭： 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Copula函数参考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的参考8= BB6 Copula函数参考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”），参考所述> =旋转（180度“生存冈贝尔”）Gumbel分布Copula的参考14=旋转乔系词（180度;“生存乔”）参考16=旋转BB1 Copula的参考17=旋转（180度“生存BB6”）BB6 Copula的参考18=旋转BB7系词（180度（180度;“BB1生存”）;“生存BB7）参考19=旋转BB8系词（180度“生存BB8”）参考20=旋转克莱顿系词（90度）参考23 =系词（90度）旋转冈贝尔参考24=旋转乔系词（90度）参考26=旋转BB1 Copula函数（90度）参考27=旋转BB6 Copula函数（90度）参考28=旋转BB7系词（90度）参考29=旋转BB8系词（90度）参考30=旋转克莱顿系词（270度）参考33=系词（270度）旋转冈贝尔参考34=旋转乔系词（270度）参考36=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：type
Type of the vine model to be specified: 0 or "RVine" = R-vine (default) 1 or "CVine" = C-vine C- and D-vine copula models with pre-specified order can be specified using CDVineCopSelect of the package CDVine. Similarly, R-vine copula models with pre-specified tree structure can be specified using RVineCopSelect.
藤模型的类型是指定： 0或"RVine"= R-藤本植物（默认）参考1或"CVine"= C-葡萄树 C-D-葡萄树的copula模型与预先指定的顺序，可以指定使用CDVineCopSelect的包CDVine。同样，R-藤Copula函数模型与预先指定的树状结构，可以指定使用RVineCopSelect。

参数：selectioncrit
Character indicating the criterion for pair-copula selection. Possible choices: selectioncrit = "AIC" (default) or "BIC" (see BiCopSelect).
字符表示对Copula函数选择的标准。可能的选择：selectioncrit = "AIC"（默认）或"BIC"（见BiCopSelect）。

参数：indeptest
Logical; whether a hypothesis test for the independence of u1 and u2 is performed before bivariate copula selection (default: indeptest = FALSE; cp. BiCopIndTest). The independence copula is chosen for a (conditional) pair if the null hypothesis of independence cannot be rejected.
逻辑，无论的假设检验的独立性u1和u2之前进行二元Copula函数的选择（默认：indeptest = FALSE; CP。BiCopIndTest）。独立Copula函数的选择（视情况而定）对独立性的零假设不能被拒绝。

参数：level
Numerical; significance level of the independence test (default: level = 0.05).
数值的独立性检验的显着性水平（默认：level = 0.05）。

参数：progress
Logical; whether the tree-wise specification progress is printed (default: progress = FALSE).
逻辑树明智的印刷规格的进展，无论（默认：progress = FALSE）。

Details

详细信息----------Details----------

R-vine trees are selected using maximum spanning trees with absolute values of pairwise Kendall's taus as weights, i.e., the following optimization problem is solved for each tree:
选择使用最大生成树的绝对值作为权数，即两两Kendall的TAUS R-藤树，每棵树下面的优化问题就解决了：

where \hat{τ}_{ij} denote the pairwise empirical Kendall's taus and a spanning tree is a tree on all nodes. The setting of the first tree selection step is always a complete graph. For subsequent trees, the setting depends on the R-vine construction principles, in particular on the proximity condition.
\hat{τ}_{ij}是指成对经验Kendall的TAUS和生成树树的所有节点上。第一树选择步骤的设置始终是一个完整的图形。对于后续的树木，设置依赖于R-藤本植物的建设原则，特别是在接近条件。

The root nodes of C-vine trees are determined similarly by identifying the node with strongest dependencies to all other nodes. That is we take the node with maximum column sum in the empirical Kendall's tau matrix.
C-藤树的根节点同样，确定最强的依存关系到所有其他节点的节点。这是我们采取的经验Kendall的tau矩阵的最大列数和在节点。

Note that a possible way to determine the order of the nodes in the D-vine is to identify a shortest Hamiltonian path in terms of weights 1-|τ_{ij}|. This can be established for example using the package TSP. Example code is shown below.
请注意，一个可能的方法来确定D-葡萄树中的节点的顺序是确定的最短的哈密尔顿路径的权重1-|τ_{ij}|。这可以建立例如使用包的TSP。示例代码如下所示。

值----------Value----------

An RVineMatrix object with the selected structure (RVM$Matrix) and families (RVM$family) as well as sequentially estimated parameters stored in RVM$par and RVM$par2.
RVineMatrix对象与所选结构（RVM$Matrix）和家庭（RVM$family）以及顺序估计的参数存储在RVM$par和RVM$par2。

（作者）----------Author(s)----------

Jeffrey Dissmann, Eike Brechmann, Ulf Schepsmeier

参考文献----------References----------

Selecting and estimating regular vine copulae and application to financial returns. Submitted for publication. http://mediatum.ub.tum.de/node?id=1079277

参见----------See Also----------

RVineTreePlot, RVineCopSelect
RVineTreePlot，RVineCopSelect

实例----------Examples----------

# load data set[加载数据集]
data(daxreturns)

# select the R-vine structure, families and parameters[选择R-葡萄树结构，家庭和参数]
## Not run: [＃不运行：]
RVM = RVineStructureSelect(daxreturns,c(1:6),progress=TRUE)

## End(Not run)[＃（不执行）]

# specify a C-vine copula model with only Clayton, Gumbel and Frank copulas[指定C-藤copula模型与克莱顿，Gumbel分布和Frank Copula函数]
## Not run: [＃不运行：]
CVM = RVineStructureSelect(daxreturns,c(3,4,5),"CVine")

## End(Not run)[＃（不执行）]

# determine the order of the nodes in a D-vine using the package TSP[确定的顺序的一个D-葡萄树中的节点使用的包的TSP]
## Not run: [＃不运行：]
library(TSP)
d = dim(daxreturns)[2]
M = 1 - abs(TauMatrix(daxreturns))
hamilton = insert_dummy(TSP(M),label="cut")
sol = solve_TSP(hamilton,method="repetitive_nn")
order = cut_tour(sol,"cut")
DVM = D2RVine(order,family=rep(0,d*(d-1)/2),par=rep(0,d*(d-1)/2))
RVineCopSelect(daxreturns,c(1:6),DVM$Matrix)

## End(Not run)[＃（不执行）]

转载请注明:出自生物统计家园网(http://www.biostatistic.net)。

注：
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