dwt(waveslim)
dwt()所属R语言包：waveslim
Discrete Wavelet Transform (DWT)
离散小波变换（DWT）
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function performs a level J decomposition of the input vector or time series using the pyramid algorithm (Mallat 1989).
这个函数执行的水平J分解的输入向量时间序列的金字塔算法（MALLAT 1989）。
用法----------Usage----------
dwt(x, wf="la8", n.levels=4, boundary="periodic")
dwt.nondyadic(x)
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).
的向量或包含数据的时间序列分解。这必须是一个二元的长度矢量（2的幂）。
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 (Daubechies, 1992), 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（Daubechies小，1992年），至少不对称的家庭。
参数：n.levels
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：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----------
The code implements the one-dimensional DWT using the pyramid algorithm (Mallat, 1989). The actual transform is performed in C using pseudocode from Percival and Walden (2001). That means convolutions, not inner products, are used to apply the wavelet filters.
该代码实现了使用金字塔算法（Mallat的，1989年）的一维DWT。实际的转换工作是在C程序中使用的伪代码由波斯富街和Walden（2001）。这意味着卷积，而不是内积，用于应用小波滤波器。
For a non-dyadic length vector or time series, dwt.nondyadic pads with zeros, performs the orthonormal DWT on this dyadic length series and then truncates the wavelet coefficient vectors appropriately.
对于非二进长向量或时间序列，dwt.nondyadic垫零，执行的正交小波变换在此二元长度系列，然后适当地截断的小波系数向量。
值----------Value----------
Basically, a list with the following components
基本上，与以下组件的列表
参数：d?
Wavelet coefficient vectors.
小波系数向量。
参数：s?
Scaling coefficient vector.
缩放系数向量。
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----------
Ten Lectures on Wavelets, CBMS-NSF Regional Conference Series in Applied Mathematics, SIAM: Philadelphia.
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----------
modwt, mra.
modwt，mra。
实例----------Examples----------
## Figures 4.17 and 4.18 in Gencay, Selcuk and Whitcher (2001).[＃数字在Gencay，塞尔丘克和Whitcher的（2001）的4.17和4.18。]
data(ibm)
ibm.returns <- diff(log(ibm))
## Haar[＃哈尔]
ibmr.haar <- dwt(ibm.returns, "haar")
names(ibmr.haar) <- c("w1", "w2", "w3", "w4", "v4")
## plot partial Haar DWT for IBM data[＃图部分哈尔DWT IBM数据]
par(mfcol=c(6,1), pty="m", mar=c(5-2,4,4-2,2))
plot.ts(ibm.returns, axes=FALSE, ylab="", main="(a)")
for(i in 1:4)
plot.ts(up.sample(ibmr.haar[[i]], 2^i), type="h", axes=FALSE,
ylab=names(ibmr.haar)[i])
plot.ts(up.sample(ibmr.haar$v4, 2^4), type="h", axes=FALSE,
ylab=names(ibmr.haar)[5])
axis(side=1, at=seq(0,368,by=23),
labels=c(0,"",46,"",92,"",138,"",184,"",230,"",276,"",322,"",368))
## LA(8)[＃LA（八）]
ibmr.la8 <- dwt(ibm.returns, "la8")
names(ibmr.la8) <- c("w1", "w2", "w3", "w4", "v4")
## must shift LA(8) coefficients[＃必须转移LA（8）系数]
ibmr.la8$w1 <- c(ibmr.la8$w1[-c(1:2)], ibmr.la8$w1[1:2])
ibmr.la8$w2 <- c(ibmr.la8$w2[-c(1:2)], ibmr.la8$w2[1:2])
for(i in names(ibmr.la8)[3:4])
ibmr.la8[[i]] <- c(ibmr.la8[[i]][-c(1:3)], ibmr.la8[[i]][1:3])
ibmr.la8$v4 <- c(ibmr.la8$v4[-c(1:2)], ibmr.la8$v4[1:2])
## plot partial LA(8) DWT for IBM data[＃图部分LA（8）DWT IBM数据]
par(mfcol=c(6,1), pty="m", mar=c(5-2,4,4-2,2))
plot.ts(ibm.returns, axes=FALSE, ylab="", main="(b)")
for(i in 1:4)
plot.ts(up.sample(ibmr.la8[[i]], 2^i), type="h", axes=FALSE,
ylab=names(ibmr.la8)[i])
plot.ts(up.sample(ibmr.la8$v4, 2^4), type="h", axes=FALSE,
ylab=names(ibmr.la8)[5])
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：
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发表于 2012-10-1 17:04:15
