ipndacw(wavethresh)
ipndacw()所属R语言包：wavethresh
Compute inner product matrix of discrete non-decimated autocorrelation wavelets.
计算内积矩阵的离散非锐减的自相关小波。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function computes the inner product matrix of discrete non-decimated autocorrelation wavelets.
此函数计算离散非锐减的自相关小波内积矩阵。
用法----------Usage----------
ipndacw(J, filter.number = 10, family = "DaubLeAsymm", tol = 1e-100, verbose
= FALSE, ...)
参数----------Arguments----------
参数：J
Dimension of inner product matrix required. This number should be a negative integer.
尺寸的内积矩阵。这个数字应该是一个负整数。
参数：filter.number
The index of the wavelet used to compute the inner product matrix.
小波的索引，用于计算内积矩阵。
参数：family
The family of wavelet used to compute the inner product matrix.
的家庭使用的小波来计算内积矩阵。
参数：tol
In the brute force computation for Daubechies compactly supported wavelets many inner product computations are performed. This tolerance discounts any results which are smaller than tol which effectively defines how long the inner product/autocorrelation products are.
在蛮力计算为Daubechies小波紧支撑小波多内积计算。这种宽容折现任何小于tol有效地确定多长时间内的产品/相关产品。
参数：verbose
If TRUE then informative messages are printed. Some of these can be quite fun as the function tells you whether precomputed matrices are being used, how much computation needs to be done and so forth.
如果TRUE然后信息会输出。其中的一些可以是相当有趣的功能会告诉您是否正在使用预先计算的矩阵，计算需要做多少等等。
参数：...
any other arguments
任何其他参数
Details
详细信息----------Details----------
This function computes the inner product matrix of the discrete non-decimated autocorrelation wavelets. This matrix is used to correct the wavelet periodogram as a step to turning it into a evolutionary wavelet spectral estimate. The matrix returned by ipndacw is the one called A in the paper by Nason, von Sachs and Kroisandt.
此函数计算内积矩阵的离散非锐减的自相关小波。该矩阵被用于校正的的小波周期图作为一个步骤，以把它成一个进化小波谱估计。该的矩阵返回ipndacw是一个称为A的文章中利晨·冯·高盛和Kroisandt的。
For the Haar wavelet the matrix is computed by using the analytical formulae in the paper by Nason, von Sachs and Kroisandt and is hence very fast and efficient and can be used for large values of -J.
Haar小波矩阵由利晨，冯萨克斯和Kroisandt的计算通过使用的文件的分析公式，因此非常快速并且高效的，可用于大的值-J。
For other Daubechies compactly supported wavelets the matrix is computed directly by autocorrelating discrete non-decimated wavelets at different scales and then forming the inner products of these. A function that computes the autocorrelation wavelets themselves is PsiJ. This brute force computation is slow and memory inefficient hence ipndacw contains a mechanism that stores any inner product matrix that it creates according to a naming scheme defined by the convention defined in rmname. The stored matrices are assigned to the current data directory. These stored matrices can be used in future computations by the following automatic procedure:
对于其他Daubechies小波紧支集小波矩阵计算直接通过在不同尺度autocorrelating离散非抽取小波，然后形成这些产品的内。一个函数，计算自相关小波自己是PsiJ。这蛮力计算是缓慢的，低效的内存，因此ipndacw包含了一种机制，用于存储任何内部的产品矩阵，它创造了根据公约中定义的rmname定义的命名方案。存储矩阵分配到当前的数据目录。在未来的计算由以下的自动程序，可以使用这些存储矩阵：
1The whole list of functions (returned by the S function search()) is searched for any previously computed inner product matrices. This search is performed by the rmget function.
（1）整个列表的功能（S功能search()）中搜索任何先前计算的内积矩阵返回。这是由搜索rmget功能。
2If a matrix of higher order is discovered then the appropriate top-left submatrix is returned, otherwise...
2如果一个高阶的矩阵被发现，那么相应的左上角子矩阵，则返回，否则......
3If the right order of matrix is found it is returned, otherwise ...
3如果按照正确的顺序矩阵，则返回，否则...
4If a matrix of smaller order is found it is used as the top-left submatrix of the answer. The remaining elements to the right of and below the submatrix are computed and then the whole matrix is returned, otherwise...
4如果发现较小的顺序的矩阵，它是用来作为答案的左上角的子矩阵。的其余元件的右边和下面的子矩阵的计算，然后被返回，否则整个矩阵...
5If none are found then the whole matrix is computed in C and returned.
5如果没有被发现，那么整个矩阵计算C和返回。
In this way a particular matrix for a given wavelet need only be computed once.
对于一个给定的小波一个特定的矩阵以这种方式只需要计算一次。
值----------Value----------
A matrix of order (-J)x(-J) containing the inner product matrix of the discrete non-decimated autocorrelation matrices.
阶矩阵的（-J）×（-J）的内积矩阵的离散非锐减的自相关矩阵。
RELEASE----------RELEASE----------
Version 3.9 Copyright Guy Nason 1998
版本3.9版权所有1998年盖利晨
（作者）----------Author(s)----------
G P Nason
参考文献----------References----------
<h3>See Also</h3> <code>ewspec</code>, <code>PsiJ</code>, <code>rmname</code>, <code>rmget</code>, <code>filter.select</code>.
实例----------Examples----------
#[]
# Let us create the 4x4 inner product matrix for the Haar wavelet.[让我们创建4x4的内积矩阵的Haar小波。]
# We'll turn on the jolly verbose messages as well. [我们将要快活的详细信息，以及。]
#[]
ipndacw(-4, filter.number=1, family="DaubExPhase", verbose=TRUE)
#Computing ipndacw[计算ipndacw]
#Calling haarmat[呼叫haarmat]
#Took 0.0699999 seconds[采取0.0699999秒]
# -1 -2 -3 -4 [-1 -2 -3 -4]
#-1 1.5000 0.7500 0.3750 0.1875[-1 1.5000 0.7500 0.3750 0.1875]
#-2 0.7500 1.7500 1.1250 0.5625[-2 0.7500 1.7500 1.1250 0.5625]
#-3 0.3750 1.1250 2.8750 2.0625[-3 0.3750 1.1250 2.8750 2.0625]
#-4 0.1875 0.5625 2.0625 5.4375[-4 0.1875 0.5625 2.0625 5.4375]
#[]
# If we do this again it will use the precomputed version[如果我们再这样做，它会使用预先计算好的版本]
#[]
ipndacw(-4, filter.number=1, family="DaubExPhase", verbose=TRUE)
#Computing ipndacw[计算ipndacw]
#Returning precomputed version: using 4 [返回预先计算版本：4]
#Took 0.08 seconds[采取0.08秒]
# -1 -2 -3 -4 [-1 -2 -3 -4]
#-1 1.5000 0.7500 0.3750 0.1875[-1 1.5000 0.7500 0.3750 0.1875]
#-2 0.7500 1.7500 1.1250 0.5625[-2 0.7500 1.7500 1.1250 0.5625]
#-3 0.3750 1.1250 2.8750 2.0625[-3 0.3750 1.1250 2.8750 2.0625]
#-4 0.1875 0.5625 2.0625 5.4375[-4 0.1875 0.5625 2.0625 5.4375]
#[]
# Let's use a smoother wavelet from the least-asymmetric family[让我们使用更顺畅的小波从至少不对称的家庭]
# and generate the 6x6 version.[并生成的6x6版本。]
#[]
ipndacw(-6, filter.number=10, family="DaubLeAsymm", verbose=TRUE)
#Computing ipndacw[计算ipndacw]
#Took 0.95 seconds[采取0.95秒]
# -1 -2 -3 -4 -5 [-1 -2 -3 -4 -5]
#-1 1.839101e+00 3.215934e-01 4.058155e-04 8.460063e-06 4.522125e-08[-1 1.839101e +00 3.215934e-01 4.058155e-04 8.460063e-06 4.522125e-08]
#-2 3.215934e-01 3.035353e+00 6.425188e-01 7.947454e-04 1.683209e-05[-2 3.215934e-01 3.035353e +00 6.425188e-01 7.947454e-04 1.683209e-05]
#-3 4.058155e-04 6.425188e-01 6.070419e+00 1.285038e+00 1.589486e-03[-3 4.058155e-04 6.425188e-01 6.070419e +00 1.285038e +00 1.589486e-03]
#-4 8.460063e-06 7.947454e-04 1.285038e+00 1.214084e+01 2.570075e+00[-4 8.460063e-06 7.947454e-04 1.285038e +00 1.214084e +01 2.570075e +00]
#-5 4.522125e-08 1.683209e-05 1.589486e-03 2.570075e+00 2.428168e+01[-5 4.522125e-08 1.683209e-05 1.589486e-03 2.570075e +00 2.428168e +01]
#-6 5.161675e-10 8.941666e-08 3.366416e-05 3.178972e-03 5.140150e+00[-6 5.161675e-10 8.941666e-08 3.366416e-05 3.178972e-03 5.140150e +00]
# -6 [-6]
#-1 5.161675e-10[-1 5.161675e-10]
#-2 8.941666e-08[-2 8.941666e-08]
#-3 3.366416e-05[-3 3.366416e-05]
#-4 3.178972e-03[-4 3.178972e-03]
#-5 5.140150e+00[-5 5.140150e +00]
#-6 4.856335e+01[-6 4.856335e +01]
#[]
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 17:22:58
