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

loveR · 发表于 2012-10-1 16:09:56

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

 Contour plot of bivariate meta distribution with different margins and copula (theoretical and empirical)
 二元元分布的等高线图具有不同的利润率和系词（理论和实证）

 译者：生物统计家园网机器人LoveR

描述----------Description----------

This function plots a bivariate contour plot corresponding to a bivariate meta distribution with different margins and specified bivariate copula and parameter values or creates corresponding empirical contour plots based on bivariate copula data.
此功能双变量对应一个二元元分布与不同的利润率和指定的二元Copula函数和参数值，或创建相应的经验基础二元Copula的数据上的等高线图等高线图绘制。

用法----------Usage----------

BiCopMetaContour(u1=NULL, u2=NULL, bw=1, size=100,
 levels=c(0.01,0.05,0.1,0.15,0.2),
 family="emp", par=0, par2=0, PLOT=TRUE,
 margins="norm", margins.par=0, xylim=NA, ...)

参数----------Arguments----------

参数：u1,u2
Data vectors of equal length with values in [0,1] (default: u1 and u2 = NULL).
数据向量长度相等的值在[0,1]（默认：u1和u2 = NULL）。

参数：bw
Bandwidth (smoothing factor; default: bw = 1).
带宽（平滑因子;默认：bw = 1）。

参数：size
Number of grid points; default: size = 100.
默认的网格点;：size = 100。

参数：levels
Vector of contour levels. For Gaussian, Student t or exponential margins the default value (levels = c(0.01,0.05,0.1,0.15,0.2)) typically is a good choice. For uniform margins we recommend levels = c(0.1,0.3,0.5,0.7,0.9,1.1,1.3,1.5) and for Gamma margins levels = c(0.005,0.01,0.03,0.05,0.07,0.09).
矢量轮廓的水平。对于高斯，学生t或指数的空间的默认值（levels = c(0.01,0.05,0.1,0.15,0.2)）通常是一个不错的选择。对于均匀利润率我们建议 的levels = c(0.1,0.3,0.5,0.7,0.9,1.1,1.3,1.5)的 和的伽玛利润 levels = c(0.005,0.01,0.03,0.05,0.07,0.09)的。

参数：family
An integer defining the bivariate copula family or indicating an empirical contour plot: "emp" = empirical contour plot (default; margins can be specified by margins) 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)
一个整数，定义二元Copula的家庭的表示的经验轮廓的图： "emp"经验等高线图（默认情况下，利润可以指定margins）参考0=独立是系词参考1=高斯Copula的参考的2学生t Copula函数（T-Copula函数）参考3=克莱顿Copula的参考4= Gumbel分布Copula函数参考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 X> = BB7 Copula的参考9= BB8系词参考10=旋转克莱顿系词（180度;“生存克莱顿”）参考13=旋转冈贝尔Copula的参考14=旋转乔系词（180度;“生存乔”）参考16=旋转BB1 Copula函数（180度（180度“生存冈贝尔”）;“生存BB1）参考17=旋转（180度“生存BB6”）BB6 Copula的参考18=旋转BB7系词（180度“生存BB7”） 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=旋转冈贝尔系词（ 90度）参考24=旋转乔系词（90度）参考26=旋转BB1 Copula的（90度）参考27=旋转BB6系词（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） 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：par
Copula parameter; if empirical contour plot, par = NULL or 0 (default).
Copula函数的参数，如果经验等高线图，par = NULL或0（默认）。

参数：par2
Second copula parameter for t-, BB1, BB6, BB7 and BB8 copulas (default: par2 = 0).
第二Copula函数的参数T-BB1，BB6，BB7和BB8 Copula函数的（默认：par2 = 0）。

参数：PLOT
Logical; whether the results are plotted. If PLOT = FALSE, the values x, y and z are returned (see below; default: PLOT = TRUE).
逻辑;结果是否绘制。如果PLOT = FALSE，值x，y和z返回（见下文;默认：PLOT = TRUE）。

参数：margins
Character; margins for the bivariate copula contour plot. Possible margins are: "norm" = standard normal margins (default) "t" = Student t margins with degrees of freedom as specified by margins.par "gamma" = Gamma margins with shape and scale as specified by margins.par "exp" = Exponential margins with rate as specified by margins.par "unif" = uniform margins
为二元Copula的等高线图的特征;利润。可能的利润是： "norm"标准的正常利润（默认）参考"t"=学生吨的利润与自由度margins.par参考"gamma" =γ利润形状和规模的指定margins.par参考"exp"指数利润率率的margins.par参考"unif"=均匀利润率

参数：margins.par
Parameter(s) of the distribution of the margins if necessary (default: margins.par = 0), i.e.,
如果有必要（默认：margins.par = 0），即利润的分配，参数（S）

a positive real number for the degrees of freedom of Student t margins (see dt),
一个正实数的程度自由的学生吨的利润（见dt），

a 2-dimensional vector of positive real numbers for the shape and scale parameters of Gamma margins (see dgamma),
一个正实数的2维矢量的形状和尺度参数的伽玛利润（见dgamma）

a positive real number for the rate parameter of exponential margins (see dexp). </ul>
一个正实数指数边距率参数（见dexp）。 </ ul>

参数：xylim
A 2-dimensional vector of the x- and y-limits. By default (xylim = NA) standard limits for the selected margins are used.
甲的2维矢量的x-和y-限制。默认情况下（xylim = NA）标准限值为选定的边缘。

参数：...
Additional plot arguments.
其他图参数。

值----------Value----------

参数：x
A vector of length size with the x-values of the kernel density estimator with Gaussian kernel if the empirical contour plot is chosen and a sequence of values in xylim if the theoretical contour plot is chosen.
一个矢量的长度sizex值的内核密度估计与高斯核经验的等高线图选择序列中的值xylim，如果选择的理论轮廓曲线。

参数：y
A vector of length size with the y-values of the kernel density estimator with Gaussian kernel if the empirical contour plot is chosen and a sequence of values in xylim if the theoretical contour plot is chosen.
一个矢量的长度sizey值的内核密度估计与高斯核经验的等高线图选择序列中的值xylim，如果选择的理论轮廓曲线。

参数：z
A matrix of dimension size with the values of the density of the meta distribution with chosen margins (see margins and margins.par) evaluated at the grid points given by x and y.
维的矩阵size选择利润率（见margins和margins.par）评估由x在网格点和元分布的密度值y。

注意----------Note----------

The combination family = 0 (independence copula) and margins = "unif" (uniform margins) is not possible because all z-values are equal.
结合family = 0（独立系词）和margins = "unif"（均匀利润率）是不可能的，因为所有的z值是相等的。

（作者）----------Author(s)----------

Ulf Schepsmeier, Alexander Bauer

参见----------See Also----------

BiCopChiPlot, BiCopKPlot, BiCopLambda
BiCopChiPlot，BiCopKPlot，BiCopLambda

实例----------Examples----------

## Example 1: contour plot of meta Gaussian copula distribution[例1：中继高斯Copula的分布的等高线图]
## with Gaussian margins[＃高斯利润与]
tau = 0.5
fam = 1
theta = BiCopTau2Par(fam,tau)
BiCopMetaContour(u1=NULL,u2=NULL,bw=1,size=100,
 levels=c(0.01,0.05,0.1,0.15,0.2),
 family=fam,par=theta,main="tau=0.5")

## Example 2: empirical contour plot with standard normal margins[＃例2：经验等高线图与标准的正常利润]
dat = BiCopSim(N=1000,fam,theta)
BiCopMetaContour(dat[,1],dat[,2],bw=2,size=100,
 levels=c(0.01,0.05,0.1,0.15,0.2),
 par=0,family="emp",main="N=1000")

# empirical contour plot with exponential margins[经验等高线图，指数利润率]
BiCopMetaContour(dat[,1],dat[,2],bw=2,size=100,
 levels=c(0.01,0.05,0.1,0.15,0.2),
 par=0,family="emp",main="n=500",
 margins="exp",margins.par=1)

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

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