R语言 wavemulcor包 wave.multiple.cross.correlation()函数中文帮助文档(中英文对照)

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wave.multiple.cross.correlation(wavemulcor)
wave.multiple.cross.correlation()所属R语言包：wavemulcor

                                         Wavelet routine for multiple cross-correlation
                                         小波多个相互关联的例程

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

Produces an estimate of the multiscale multiple cross-correlation (as defined below).
产生一个估计的多尺度多交叉相关（定义见下文）。


用法----------Usage----------


wave.multiple.cross.correlation(xx, lag.max = NULL, ymaxr = NULL)



参数----------Arguments----------

参数：xx
A list of n (multiscaled) time series, usually the outcomes of dwt or modwt, i.e. xx <- list(v1.modwt.bw, v2.modwt.bw, v3.modwt.bw)
n（multiscaled）的时间序列列表，通常的结果的载重吨或modwt的，即XX < - 列表（v1.modwt.bw，v2.modwt.bw，v3.modwt.bw）


参数：lag.max
maximum lag. If not set, it defaults to half the square root of the length of the original series.
最高滞后。如果不设置，则默认为一半的平方根的长度在原始的系列。


参数：ymaxr
index number of the variable whose correlation is calculated against a linear combination of the rest, otherwise at each wavelet level wmc chooses the one maximizing the multiple correlation.
索引号的变量的相关计算针对其余的线性组合，否则在每个小波一级WMC选择一个最大化的多个相关。


Details

详细信息----------Details----------

The routine calculates one single set of wavelet multiple cross-correlations out of n variables that can be plotted as one single set of graphs (one per wavelet level), as an alternative to trying to make sense out of n(n-1)/2 x J sets of wavelet cross-correlations. The code is based on the calculation, at each wavelet scale, of the square root of the coefficient of determination in a linear combination of variables that includes a lagged variable for which such coefficient of determination is a maximum.   
该例程的小波的多个交叉相关计算出一个单一的一套满分n变量可以绘制成一个单一的一套图（1每小波级别），作为一种替代，以试图使感满分<X >组的小波交叉相关。该代码是基于有关计算，在每个小波规模，滞后该判定系数为最大的变量的变量的线性组合，其中包括的确定系数的平方根。


值----------Value----------

List of two elements:<br> xy.mulcor: matrix with as many rows as levels in the wavelet transform object. The columns provide the point estimates for the wavelet multiple cross-correlations at different lags.<br> YmaxR: numeric vector giving, at each wavelet level, the index number of the variable whose correlation is calculated against a linear combination of the rest. By default, wmcc chooses at each wavelet level the variable maximizing the multiple correlation.
两个元素的列表的参考xy.mulcor：矩阵尽可能多的行水平的小波变换对象。列提供的点估计为小波多个交叉相关在不同的滞后。参考YmaxR：数值向量给，针对其余的线性组合来计算的相关的变量的索引号，在每个小波一级。默认情况下，每个小波wmcc选择多个相关变量的最大化。


注意----------Note----------

Needs waveslim package to calculate dwt or modwt coefficients as inputs to the routine (also for data in the example).
需要waveslim包计算输入例程（在本例中的数据）的的载重吨，modwt系数为。


（作者）----------Author(s)----------



Javier Fern谩ndez-Macho, Dpt. of Econometrics and Statistics,
&amp; Instituto de Econom铆a P煤blica,
University of the Basque Country,
Agirre Lehendakari etorb. 83, E48015 BILBAO, Spain. (email: javier.fernandezmacho@ehu.es).




参考文献----------References----------

Fern谩ndez-Macho, Javier (2011) The wavelet multiple correlation, (mimeo).

实例----------Examples----------


## Based on data from Figure 7.9 in Gencay, Selcuk and Whitcher (2001)[＃从图7.9中Gencay，塞尔丘克和Whitcher的数据（2001年）]
## plus one random series.[＃加上一个随机序列。]

library(wavemulcor)
data(exchange)
returns <- diff(log(exchange))
returns <- ts(returns, start=1970, freq=12)
wf <- "d4"
J <- 6
lmax <- 36
n <- dim(returns)[1]

demusd.modwt <- modwt(returns[,"DEM.USD"], wf, J)
demusd.modwt.bw <- brick.wall(demusd.modwt, wf)
jpyusd.modwt <- modwt(returns[,"JPY.USD"], wf, J)
jpyusd.modwt.bw <- brick.wall(jpyusd.modwt, wf)
rand.modwt <- modwt(rnorm(length(returns[,"DEM.USD"])), wf, J)
rand.modwt.bw <- brick.wall(rand.modwt, wf)

##xx &lt;- list(demusd.modwt.bw, jpyusd.modwt.bw)[＃XX < - 的列表（demusd.modwt.bw，jpyusd.modwt.bw）]
xx <- list(demusd.modwt.bw, jpyusd.modwt.bw, rand.modwt.bw)

Lst <- wave.multiple.cross.correlation(xx, lmax)
returns.cross.cor <- as.matrix(Lst$xy.mulcor[1:J,])
YmaxR <- Lst$YmaxR

exchange.names <- c("DEM.USD", "JPY.USD", "RAND")
rownames(returns.cross.cor)<-rownames(returns.cross.cor,
  do.NULL = FALSE, prefix = "Level ")
lags <- length(-lmax:lmax)

lower.ci <- tanh(atanh(returns.cross.cor) - qnorm(0.975) /
sqrt(matrix(trunc(n/2^(1:J)), nrow=J, ncol=lags)- 3))
upper.ci <- tanh(atanh(returns.cross.cor) + qnorm(0.975) /
sqrt(matrix(trunc(n/2^(1:J)), nrow=J, ncol=lags)- 3))

par(mfrow=c(3,2), las=1, pty="m", mar=c(2,3,1,0)+.1, oma=c(1.2,1.2,0,0))
for(i in J:1) {
matplot((12*lmax+1)),returns.cross.cor[i,], type="l", lty=1, ylim=c(-1,1),
  xaxt="n", xlab="", ylab="", main=rownames(returns.cross.cor)[[i]][1])
if(i<3) {axis(side=1, at=seq(1, 2*lmax+1, by=12),
  labels=seq(-lmax, lmax, by=12))}
#axis(side=2, at=c(-.2, 0, .5, 1))[轴（侧= 2，= C（ -  0.2，0 0.5，1））]
lines(lower.ci[i,], lty=1, col=2) ##Add Connected Line Segments to a Plot[＃添加连接线段的地积]
lines(upper.ci[i,], lty=1, col=2)
abline(h=0,v=lmax+1)              ##Add Straight horiz and vert Lines to a Plot[＃直HORIZ线和垂直线的地积]
text(1,1, labels=exchange.names[YmaxR[i]], adj=0.25, cex=.8)
}
par(las=0)
mtext('Lag (months)', side=1, outer=TRUE, adj=0.5)
mtext('Wavelet Multiple Cross-Correlation', side=2, outer=TRUE, adj=0.5)

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


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