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

loveR · 发表于 2012-10-1 16:11:25

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

 Sequential copula selection and estimation of R-vine copula models
 连续Copula函数的选择和藤R-Copula函数模型的估计

 译者：生物统计家园网机器人LoveR

描述----------Description----------

This function fits a R-vine copula model to a d-dimensional copula data set. Pair-copula families are selected using BiCopSelect and estimated sequentially.
此功能适合R-葡萄树的copula模型d维Copula的数据集。对Copula的家庭选择使用BiCopSelect，估计顺序。

用法----------Usage----------

RVineCopSelect(data, familyset=NA, Matrix, selectioncrit="AIC",
 indeptest=FALSE, level=0.05)

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. The coding of pair-copula families is shown below.
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。成对的copula家庭的编码如下所示。

参数：Matrix
Lower triangular d x d matrix that defines the R-vine tree structure.
下三角的DXD矩阵，定义的R-葡萄树的树结构。

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; see. 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;。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
Numeric; significance level of the independence test (default: level = 0.05).
数字的独立性检验的显着性水平（默认：level = 0.05）。

Details

详细信息----------Details----------

R-vine copula models with unknown structure can be specified using RVineStructureSelect.
R-藤Copula函数模型的未知结构可以指定使用RVineStructureSelect。

值----------Value----------

An RVineMatrix object with the following matrix components
RVineMatrix对象与下面的矩阵组成部分

参数：Matrix
R-vine tree structure matrix as given by the argument Matrix.
R-葡萄树结构矩阵所给出的参数Matrix。

参数：family
Selected pair-copula family matrix with values corresponding to 0 = independence copula 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)
对Copula函数与值对应至 的家庭矩阵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） 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的参考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BB1 Copula函数（180度;”BB1生存“）参考18=旋转BB6 Copula函数（180度”生存BB6“的）参考19=旋转BB7 Copula的参考20=旋转BB8系词（180度（180度“生存BB7”的），“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系词（90度）参考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=旋转（270度）Gumbel分布Copula的参考36=旋转乔系词（270度）参考37=旋转BB1 Copula函数（270度）参考38=旋转BB6 Copula函数（270度）参考39=旋转BB7系词（270度） BR> 40=旋转BB8系词（270度）

参数：par
Estimated pair-copula parameter matrix.
估计对Copula函数的参数矩阵。

参数：par2
Estimated second pair-copula parameter matrix with parameters of pair-copula families with two parameters.
预计第二对Copula函数的参数矩阵参数对Copula的家庭的两个参数。

（作者）----------Author(s)----------

Eike Brechmann

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----------

RVineStructureSelect, BiCopSelect, RVineSeqEst
RVineStructureSelect，BiCopSelect，RVineSeqEst

实例----------Examples----------

# define 5-dimensional R-vine tree structure matrix[定义5维的R-葡萄树结构矩阵]
Matrix = c(5,2,3,1,4,0,2,3,4,1,0,0,3,4,1,0,0,0,4,1,0,0,0,0,1)
Matrix = matrix(Matrix,5,5)

# define R-vine pair-copula family matrix[定义R-藤对Copula的家庭矩阵]
family = c(0,1,3,4,4,0,0,3,4,1,0,0,0,4,1,0,0,0,0,3,0,0,0,0,0)
family = matrix(family,5,5)

# define R-vine pair-copula parameter matrix[定义R-藤对Copula函数的参数矩阵]
par = c(0,0.2,0.9,1.5,3.9,0,0,1.1,1.6,0.9,0,0,0,1.9,0.5,
 0,0,0,0,4.8,0,0,0,0,0)
par = matrix(par,5,5)

# define second R-vine pair-copula parameter matrix[定义第二个R-藤对Copula函数的参数矩阵]
par2 = matrix(0,5,5)

# define RVineMatrix object[定义RVineMatrix对象]
RVM = RVineMatrix(Matrix=Matrix,family=family,par=par,par2=par2,
 names=c("V1","V2","V3","V4","V5"))

# simulate a sample of size 1000 from the R-vine copula model[从R-藤Copula模型模拟的样本大小为1000]
simdata = RVineSim(1000,RVM)

# determine the pair-copula families and parameters[确定的对Copula的家庭，和参数]
RVM1 = RVineCopSelect(simdata,familyset=c(1,3,4,5,6),Matrix)

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

注：
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