mra(waveslim)
mra()所属R语言包：waveslim
Multiresolution Analysis of Time Series
多分辨分析的时间序列
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function performs a level J additive decomposition of the input vector or time series using the pyramid algorithm (Mallat 1989).
这个函数执行的水平J添加剂的分解，输入向量或使用金字塔算法（MALLAT 1989年）的时间序列。
用法----------Usage----------
参数----------Arguments----------
参数：x
A vector or time series containing the data be to decomposed. This must be a dyadic length vector (power of 2) for method="dwt".
包含的数据是一个向量或时间序列分解。这必须是一个二元的长度矢量（2的幂）method="dwt"。
参数：wf
Name of the wavelet filter to use in the decomposition. By default this is set to "la8", the Daubechies orthonormal compactly supported wavelet of length L=8 least asymmetric family.
的小波滤波器的名称使用在分解。默认情况下，此设置为"la8"的Daubechies正交的紧支撑小波的长度L=8至少不对称家庭。
参数：J
Specifies the depth of the decomposition. This must be a number less than or equal to log(length(x),2).
指定的深度的分解。这必须是一个小于或等于log(length(x),2)。
参数：method
Either "dwt" or "modwt".
无论是"dwt"或"modwt"。
参数：boundary
Character string specifying the boundary condition. If boundary=="periodic" the default, then the vector you decompose is assumed to be periodic on its defined interval,<br> if boundary=="reflection", the vector beyond its boundaries is assumed to be a symmetric reflection of itself.
字符串指定的边界条件。如果boundary=="periodic"默认值，则假定矢量分解其定义的时间间隔上是周期性的，<br>请如果boundary=="reflection"，超出其边界的矢量假定是一个对称反射本身。
Details
详细信息----------Details----------
This code implements a one-dimensional multiresolution analysis introduced by Mallat (1989). Either the DWT or MODWT may be used to compute the multiresolution analysis, which is an additive decomposition of the original time series.
此代码实现了一个一维的引入的MALLAT（1989）的多分辨分析。使用，无论是载重吨或MODWT可以计算的多分辨率分析，这是原始的时间序列的添加剂分解。
值----------Value----------
Basically, a list with the following components
基本上，与以下组件的列表
参数：D?
Wavelet detail vectors.
小波细节向量。
参数：S?
Wavelet smooth vector.
小波光滑向量。
参数：wavelet
Name of the wavelet filter used.
小波滤波器的名称。
参数：boundary
How the boundaries were handled.
的界限如何处理。
（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.
A theory for multiresolution signal decomposition: the wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, No. 7, 674-693.
Wavelet Methods for Time Series Analysis, Cambridge University Press.
参见----------See Also----------
dwt, modwt.
dwt，modwt。
实例----------Examples----------
## Easy check to see if it works...[简单检查，看它是否正常工作...]
x <- rnorm(32)
x.mra <- mra(x)
sum(x - apply(matrix(unlist(x.mra), nrow=32), 1, sum))^2
## Figure 4.19 in Gencay, Selcuk and Whitcher (2001)[＃图4.19 Gencay，塞尔丘克和Whitcher的（2001）]
data(ibm)
ibm.returns <- diff(log(ibm))
ibm.volatility <- abs(ibm.returns)
## Haar[＃哈尔]
ibmv.haar <- mra(ibm.volatility, "haar", 4, "dwt")
names(ibmv.haar) <- c("d1", "d2", "d3", "d4", "s4")
## LA(8)[＃LA（八）]
ibmv.la8 <- mra(ibm.volatility, "la8", 4, "dwt")
names(ibmv.la8) <- c("d1", "d2", "d3", "d4", "s4")
## plot multiresolution analysis of IBM data[＃图IBM数据多分辨率分析]
par(mfcol=c(6,1), pty="m", mar=c(5-2,4,4-2,2))
plot.ts(ibm.volatility, axes=FALSE, ylab="", main="(a)")
for(i in 1:5)
plot.ts(ibmv.haar[[i]], axes=FALSE, ylab=names(ibmv.haar)[i])
axis(side=1, at=seq(0,368,by=23),
labels=c(0,"",46,"",92,"",138,"",184,"",230,"",276,"",322,"",368))
par(mfcol=c(6,1), pty="m", mar=c(5-2,4,4-2,2))
plot.ts(ibm.volatility, axes=FALSE, ylab="", main="(b)")
for(i in 1:5)
plot.ts(ibmv.la8[[i]], axes=FALSE, ylab=names(ibmv.la8)[i])
axis(side=1, at=seq(0,368,by=23),
labels=c(0,"",46,"",92,"",138,"",184,"",230,"",276,"",322,"",368))
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 17:07:00
