AvBasis.wst(wavethresh)
AvBasis.wst()所属R语言包：wavethresh
Perform basis averaging for (packet-ordered) non-decimated wavelet transform.
执行依据平均（包有序的）非抽取小波变换。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Perform basis averaging for (packet-ordered) non-decimated wavelet transform.
执行依据平均（包有序的）非抽取小波变换。
用法----------Usage----------
## S3 method for class 'wst':
AvBasis(wst, Ccode=TRUE, ...)
参数----------Arguments----------
参数：wst
An object of class wst that contains coefficients of a packet ordered non-decimated wavelet transform (e.g. produced by the wst function.
类的一个对象wst包含的数据包下令非抽取小波变换（例如，wst功能的系数。
参数：Ccode
If TRUE then fast compiled C code is used to perform the transform. If FALSE then S code is used. Almost always use the default TRUE option. (It is conceivable that some implementation can not use the C code and so this option permits use of the slower S code).
如果为真，那么快速编译的C代码是用来执行转换。如果FALSE则S代码。几乎总是使用默认的TRUE选项。 （这是可以想象的实施，一些不能使用的C代码，所以这个选项允许使用较慢的S码）。
参数：...
any other arguments
任何其他参数
Details
详细信息----------Details----------
The packet-ordered non-decimated wavelet transform computed by wst computes the coefficients of an input vector with respect to a library of all shifts of wavelet basis functions at all scales. Here "all shifts" means all integral shifts with respect to the finest scale coefficients, and "all scales" means all dyadic scales from 0 (the coarsest) to J-1 (the finest) where 2^J = n where n is the number of data points of the input vector. As such the packet-ordered non-decimated wavelet transform contains a library of all possible shifted wavelet bases.
数据包排序，非抽取小波变换计算的wst计算系数在所有尺度上的所有班次的小波基函数库的输入向量。这里的“所有班次”是指所有积分的变化与尊重的最好的尺度系数，“所有尺度”是指所有二进尺度从0（最粗），J-1（最好）2^J = n其中<X >是输入矢量的数据点的数目。这样的分组排序，非抽取小波变换包含所有可能的移小波基库。
Basis selection It is possible to select a particular basis and invert that particular representation. In WaveThresh a basis is selected by creating a nv (node.vector) class object which identifies the basis. Then the function InvBasis takes the wavelet representation and the node.vector and inverts the representation with respect to the selected basis. The two functions MaNoVe and numtonv create a node.vector: the first by using a Coifman-Wickerhauser minimum entropy best-basis algorithm and the second by basis index.
Basis selection它是可以选择一个特定的基础和反转该特定表示。在WaveThresh的基础是选择通过创建一个nv（node.vector）类对象，该对象标识的基础上。然后，该功能InvBasis需要小波表示和node.vector的反转表示相对于所选的基础。这两个函数MaNoVe和numtonv创建一个node.vector的：第一，用最低的熵最基础的算法和基础指数的第二Coifman-Wickerhauser。
Basis averaging. Rather than select a basis it is often useful to preserve information from all of the bases. For examples, in curve estimation, after thresholding a wavelet representation the coefficients are coefficients of an estimate of the truth with respect to all of the shifted basis functions. Rather than select one of them we can average over all estimates. This sometimes gives a better curve estimate and can, for examples, get rid of Gibbs effects. See Coifman and Donoho (1995) for more information on how to do curve estimation using the packet ordered non-decimated wavelet transform, thresholding and basis averaging.
基准的平均值。而不是选择的基础，它是非常有用的信息保存碱基。曲线估计中，对于实施例中，阈值的小波表示后的系数的系数的估计值相对于所有的移位的基函数的真理。而不是选择其中之一，我们可以平均超过所有的估计。有时，这给出了一个更好的曲线估计的例子，可以摆脱吉布斯效应。 Coifman和Donoho提出（1995年）的更多信息，如何使用包下令非抽取小波变换，阈值和基础平均曲线估计。
Further it might seem that inverting each wavelet transform and averaging might be a computationally expensive operation: since each wavelet inversion costs order n operations and there are n different bases and so you might think that the overall order is n^2. It turns out that since many of the coarser scale basis functions are duplicated between bases there is redundancy in the non-decimated transform. Coifman and Donoho's TI-denoising algorithm makes use of this redundancy which results in an algorithm which only takes order n*logn operations.
此外，它可能似乎是反相的每个小波变换和平均可能是一个昂贵的操作计算：由于各子波反演讼费的命令，n操作有n个不同的基础，所以你可能会认为，整体的顺序是n^2 。事实证明，因为许多的较粗的尺度的基函数是重复的碱基之间存在冗余的非抽取变换。 Coifman和Donoho提出的的TI-去噪算法使用的算法只需要为了n*logn操作而导致这种冗余。
For an examples of denoising using the packet-ordered non-decimated wavelet transform and basis averaging see Johnstone and Silverman, 1997. The WaveThresh implementation of the basis averaging algorithm is to be found in Nason and Silverman, 1995
对的的去噪方法分组排序的非抽取小波变换和基础平均约翰斯通和Silverman，1997年的例子。被发现在利晨和Silverman，1995年的基础上平均算法是WaveThresh实施
值----------Value----------
A vector containing the average of the wavelet representation over all the basis functions. The length of the vector is 2^nlev where nlev is the number of levels in the input wst object.
一种向量，含有超过所有的基础函数的小波表示平均。的向量的长度是2^nlevnlev是在输入wst对象的级别数。
RELEASE----------RELEASE----------
Version 3.6.0 Copyright Guy Nason 1995
版本3.6.0版权所有1995年盖利晨
（作者）----------Author(s)----------
G P Nason
参见----------See Also----------
av.basis, wst, wst.object, MaNoVe, numtonv, InvBasis, wavegrow
av.basis，wst，wst.object，MaNoVe，numtonv，InvBasis，wavegrow
实例----------Examples----------
#[]
# Generate some test data[生成一些测试数据。]
#[]
test.data <- example.1()$y
#[]
# Now take the packet-ordered non-decimated wavelet transform[现在采取的有序分组的非抽取小波变换]
#[]
tdwst <- wst(test.data)
#[]
# Now "invert" it using basis averaging[现在，“反转”基础上平均]
#[]
tdwstAB <- AvBasis(tdwst)
#[]
# Let's compare it to the original[让我们来比较一下原来的]
#[]
sum( (tdwstAB - test.data)^2)
#[]
# [1] 9.819351e-17 [[1] 9.819351e-17]
#[]
# Very small. They're essentially same.[非常小的。他们本质上是相同的。]
#[]
# See the threshold.wst help page for an[请参阅帮助页面的threshold.wst]
# an examples of using basis averaging in curve estimation.[使用曲线估计的基础上平均的一个例子。]
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。
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发表于 2012-10-1 17:12:50
