wave.variance(waveslim)
wave.variance()所属R语言包：waveslim
Wavelet Analysis of Univariate/Bivariate Time Series
一元/二元时间序列的小波分析
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Produces an estimate of the multiscale variance, covariance or correlation along with approximate confidence intervals.
产生一个估计的方差，协方差或相关的多尺度以及具有近似的置信区间。
用法----------Usage----------
wave.covariance(x, y)
wave.correlation(x, y, N, p=0.975)
参数----------Arguments----------
参数：x
first time series
第一个时间序列
参数：y
second time series
第二个时间序列
参数：type
character string describing confidence interval calculation; valid methods are gaussian, eta1, eta2, eta3, nongaussian
字符串描述的置信区间的计算;有效的方法是gaussian，eta1，eta2，eta3，nongaussian
参数：p
(one minus the) two-sided p-value for the confidence interval
（一减）双面p值的置信区间
参数：N
length of time series
时间序列的长度
Details
详细信息----------Details----------
The time-independent wavelet variance is basically the average of the squared wavelet coefficients across each scale. As shown in Percival (1995), the wavelet variance is a scale-by-scale decomposition of the variance for a stationary process, and certain non-stationary processes.
与时间无关的小波方差基本上横跨每个尺度的小波系数的平方的平均值。正如波斯富街（1995）中所示，小波方差是方差的规模由尺度分解为一个固定的过程中，某些非平稳过程。
值----------Value----------
Matrix with as many rows as levels in the wavelet transform object. The first column provides the point estimate for the wavelet variance, covariance, or correlation followed by the lower and upper bounds from the confidence interval.
矩阵与尽可能多的行小波变换对象的水平。的第一列提供了后跟的置信区间的下限和上限从小波方差，协方差，或相关的点估计值。
（作者）----------Author(s)----------
B. Whitcher
参考文献----------References----------
An Introduction to Wavelets and Other Filtering Methods in Finance and Economics, Academic Press.
Biometrika, 82, No. 3, 619-631.
Wavelet Methods for Time Series Analysis, Cambridge University Press.
Wavelet Analysis of Covariance with Application to Atmospheric Time Series, Journal of Geophysical Research, 105, No. D11, 14,941-14,962.
实例----------Examples----------
## Figure 7.3 from Gencay, Selcuk and Whitcher (2001)[＃图7.3 Gencay，塞尔丘克和Whitcher的（2001年）]
data(ar1)
ar1.modwt <- modwt(ar1, "haar", 6)
ar1.modwt.bw <- brick.wall(ar1.modwt, "haar")
ar1.modwt.var2 <- wave.variance(ar1.modwt.bw, type="gaussian")
ar1.modwt.var <- wave.variance(ar1.modwt.bw, type="nongaussian")
par(mfrow=c(1,1), las=1, mar=c(5,4,4,2)+.1)
matplot(2^(0:5), ar1.modwt.var2[-7,], type="b", log="xy",
xaxt="n", ylim=c(.025, 6), pch="*LU", lty=1, col=c(1,4,4),
xlab="Wavelet Scale", ylab="")
matlines(2^(0:5), as.matrix(ar1.modwt.var)[-7,2:3], type="b",
pch="LU", lty=1, col=3)
axis(side=1, at=2^(0:5))
legend(1, 6, c("Wavelet variance", "Gaussian CI", "Non-Gaussian CI"),
lty=1, col=c(1,4,3), bty="n")
## Figure 7.8 from Gencay, Selcuk and Whitcher (2001)[＃从Gencay，塞尔丘克和Whitcher的图7.8（2001年）]
data(exchange)
returns <- diff(log(as.matrix(exchange)))
returns <- ts(returns, start=1970, freq=12)
wf <- "d4"
J <- 6
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)
returns.modwt.cov <- wave.covariance(demusd.modwt.bw, jpyusd.modwt.bw)
par(mfrow=c(1,1), las=0, mar=c(5,4,4,2)+.1)
matplot(2^(0 J-1)), returns.modwt.cov[-(J+1),], type="b", log="x",
pch="*LU", xaxt="n", lty=1, col=c(1,4,4), xlab="Wavelet Scale",
ylab="Wavelet Covariance")
axis(side=1, at=2^(0:7))
abline(h=0)
returns.modwt.cor <- wave.correlation(demusd.modwt.bw, jpyusd.modwt.bw,
N = dim(returns)[1])
par(mfrow=c(1,1), las=0, mar=c(5,4,4,2)+.1)
matplot(2^(0J-1)), returns.modwt.cor[-(J+1),], type="b", log="x",
pch="*LU", xaxt="n", lty=1, col=c(1,4,4), xlab="Wavelet Scale",
ylab="Wavelet Correlation")
axis(side=1, at=2^(0:7))
abline(h=0)
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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。
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发表于 2012-10-1 17:10:04
