BiCopChiPlot(VineCopula)
BiCopChiPlot()所属R语言包：VineCopula
Chi-plot for bivariate copula data
智积二元Copula的数据
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function creates a chi-plot of given bivariate copula data.
这个函数创建一个图给定的二元Copula的数据卡。
用法----------Usage----------
BiCopChiPlot(u1, u2, PLOT=TRUE, mode="NULL", ...)
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：PLOT
Logical; whether the results are plotted. If PLOT = FALSE, the values lambda, chi and control.bounds are returned (see below; default: PLOT = TRUE).
逻辑;结果是否绘制。如果PLOT = FALSE，值lambda，chi和control.bounds返回（见下文;默认：PLOT = TRUE）。
参数：mode
Character; whether a general, lower or upper chi-plot is calculated. Possible values are mode = "NULL", "upper" and "lower". <br> "NULL" = general chi-plot (default)<br> "upper" = upper chi-plot<br> "lower" = lower chi-plot
字符，无论是一般情况下，降低或计算上的卡图。可能的值mode = "NULL"，"upper"和"lower"。参考"NULL" =通用卡的图（默认）参考"upper"=上智积<BR>"lower"=低智积
参数：...
Additional plot arguments.
其他图参数。
Details
详细信息----------Details----------
For observations u_{i,j}, i=1,...,N, j=1,2, the chi-plot is based on the following two quantities: the chi-statistics
的观测u_{i,j}, i=1,...,N, j=1,2,卡图是基于如下的两个量：卡统计
and the lambda-statistics
和λ-统计
where F_{U_1}, F_{U_2} and F_{U_1U_2} are the empirical distribution functions of the uniform random variables U_1 and U_2 and of (U_1,U_2), respectively. Further, tildeF_{U_1}=F_{U_1}-0.5 and tildeF_{U_2}=F_{U_2}-0.5.
其中F_{U_1}，F_{U_2}和F_{U_1U_2}是均匀分布的随机变量U_1和U_2和(U_1,U_2)，经验分布函数的。此外，tildeF_{U_1}=F_{U_1}-0.5和tildeF_{U_2}=F_{U_2}-0.5。
These quantities only depend on the ranks of the data and are scaled to the interval [0,1]. λ_i measures a distance of a data point (u_{i,1},u_{i,2}) to the center of the bivariate data set, while χ_i corresponds to a correlation coefficient between dichotomized values of U_1 and U_2. Under independence it holds that χ_i~N(0,1/N) and λ_i~U[0,1] asymptotically, i.e., values of χ_i close to zero indicate independence—corresponding to F_{U_1U_2}=F_{U_1}F_{U_2}.
这些数量仅依赖的数据的行列，并缩放的时间间隔[0,1]。 λ_i测量数据点(u_{i,1},u_{i,2})的二元数据集的中心的距离，而χ_i对应二分法U_1和<X的值之间的相关系数>。在独立认为这U_2和χ_i~N(0,1/N)渐近，即，值λ_i~U[0,1]接近于零表明独立对应的χ_i。
When plotting these quantities, the pairs of (λ_i,χ_i) will tend to be located above zero for positively dependent margins and vice versa for negatively dependent margins. Control bounds around zero indicate whether there is significant dependence present.
当绘制这些量，对(λ_i,χ_i)将趋于上方为零，利润为负相关正依赖的利润，反之亦然。在零附近的控制界限是否有显着的依赖，目前。
If mode = "lower" or "upper", the above quantities are calculated only for those u_{i,1}'s and u_{i,2}'s which are smaller/larger than the respective means of u1=(u_{1,1},...,u_{N,1}) and u2=(u_{1,2},...,u_{N,2}).
如果mode = "lower"或"upper"，上述数量是仅用于计算这些u_{i,1}s和u_{i,2}的是小于/大于各自的装置u1 =(u_{1,1},...,u_{N,1})和的u2“=(u_{1,2},...,u_{N,2})。
值----------Value----------
参数：lambda
Lambda-statistics (x-axis).
λ-统计量（x轴）。
参数：chi
Chi-statistics (y-axis).
智统计（y轴）。
参数：control.bounds
A 2-dimensional vector of bounds ((1.54/√{n},-1.54/√{n}), where n is the length of u1 and where the chosen values correspond to an approximate significance level of 10%.
界甲2维向量((1.54/√{n},-1.54/√{n})，其中n是u1的长度，以及其中所选择的值对应于近似为10％的显着性水平。
（作者）----------Author(s)----------
Natalia Belgorodski, Ulf Schepsmeier
参考文献----------References----------
A simple graphical method to explore tail-dependence in stock-return pairs. Discussion Paper, University of Konstanz, Germany.
Everything you always wanted to know about copula modeling but were afraid to ask. Journal of Hydrologic Engineering, 12 (4), 347-368.
参见----------See Also----------
BiCopMetaContour, BiCopKPlot, BiCopLambda
BiCopMetaContour，BiCopKPlot，BiCopLambda
实例----------Examples----------
# chi-plots for bivariate Gaussian copula data[二元高斯Copula的数据卡图]
n = 500
tau = 0.5
# simulate copula data[模拟Copula的数据]
fam = 1
theta = BiCopTau2Par(fam,tau)
dat = BiCopSim(n,fam,theta)
# create chi-plots[创建卡图]
dev.new(width=16,height=5)
par(mfrow=c(1,3))
BiCopChiPlot(dat[,1],dat[,2],xlim=c(-1,1),ylim=c(-1,1),
main="General chi-plot")
BiCopChiPlot(dat[,1],dat[,2],mode="lower",xlim=c(-1,1),
ylim=c(-1,1),main="Lower chi-plot")
BiCopChiPlot(dat[,1],dat[,2],mode="upper",xlim=c(-1,1),
ylim=c(-1,1),main="Upper chi-plot")
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。
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发表于 2012-10-1 16:08:45
