PsiJ(wavethresh)
PsiJ()所属R语言包：wavethresh
Compute discrete autocorrelation wavelets.
计算离散自相关小波。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function computes discrete autocorrelation wavelets.
自相关函数计算离散小波。
The inner products of the discrete autocorrelation wavelets are computed by the routine ipndacw.
离散自相关小波的内积的计算例程ipndacw。
用法----------Usage----------
PsiJ(J, filter.number = 10, family = "DaubLeAsymm", tol = 1e-100, OPLENGTH=2000)
参数----------Arguments----------
参数：J
Discrete autocorrelation wavelets will be computed for scales -1 up to scale J. This number should be a negative integer.
将计算离散自相关小波尺度-1起来，规模J.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 discrete autocorrelation wavelets.
小波的索引用于计算离散自相关小波。
参数：family
The family of wavelet used to compute the discrete autocorrelation wavelets.
的家庭使用的小波来计算离散自相关的小波。
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：OPLENGTH
This integer variable defines some workspace of length OPLENGTH. The code uses this workspace. If the workspace is not long enough then the routine will stop and probably tell you what OPLENGTH should be set to.
这个整数变量定义的长度OPLENGTH一些工作区。该代码使用此工作区。如果工作区是不是足够长的时间，那么程序将停止，并可能会告诉你什么OPLENGTH应设置为。
Details
详细信息----------Details----------
This function computes the discrete autocorrelation wavelets. It does not have any direct use for time-scale analysis (e.g. ewspec). However, it is useful to be able to numerically compute the discrete autocorrelation wavelets for arbitrary wavelets and scales as there are still unanswered theoretical questions concerning the wavelets. The method is a brute force – a more elegant solution would probably be based on interpolatory schemes.
此函数计算离散自相关小波。它没有任何直接用于时间尺度分析（如：ewspec）。但是，它是有用的，能够任意小波和尺度数值计算的离散自相关小波小波理论问题仍然没有答案。该方法是一种暴力 - 一个更优雅的解决方案可能会根据插值方案。
Horizontal scale. This routine returns only the values of the discrete autocorrelation wavelets and not their horiztonal positions. Each discrete autocorrelation wavelet is compactly supported with the support determined from the compactly supported wavelet that generates it. See the paper by Nason, von Sachs and Kroisandt which defines the horiztonal scale (but basically the finer scale discrete autocorrelation wavelets are interpolated versions of the coarser ones. When one goes from scale j to j-1 (negative j remember) an extra point is inserted between all of the old points and the discrete autocorrelation wavelet value is computed there. Thus as J tends to negative infinity the numerical approximation tends towards the continuous autocorrelation wavelet.
水平刻度。此例程返回的值离散自相关小波，而不是horiztonal的位置。每一个离散自相关小波紧支撑确定从产生它的紧支撑小波的支持。请参阅，由利晨，冯萨克斯和Kroisandt的这定义的horiztonal的规模（但基本上较细规模离散自相关小波内插的较粗的版本，当一个人从尺度j，j-1的（记得负Ĵ）一个额外的点纸插入所有旧了点之间的离散自相关小波值计算。因此，为J趋向于负无穷大的数值逼近趋于连续自相关小波。
This function stores any discrete autocorrelation wavelet sets that it computes. The storage mechanism is not as advanced as that for ipndacw and its subsidiary routines rmget and firstdot but helps a little bit. The Psiname function defines the naming convention for objects returned by this function.
此功能将把任何离散自相关小波集计算。 ipndacw和其附属公司的过程rmget和firstdot的存储机制不先进，但有助于一点点。 Psiname函数定义这个函数返回的对象的命名约定。
Sometimes it is useful to have the discrete autocorrelation wavelets stored in matrix form. The PsiJmat does this.
有时，它是非常有用的离散的自相关矩阵形式存储在小波。 PsiJmat这样做了。
值----------Value----------
A list containing -J components, numbered from 1 to -J. The [[j]]th component contains the discrete autocorrelation wavelet at scale j.
列表包含-J组件，编号从1-J。 [[J]个组件中包含的离散自相关小波在尺度j。
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>ipndacw</code>, <code>PsiJmat</code>, <code>Psiname</code>.
实例----------Examples----------
#[]
# Let us create the discrete autocorrelation wavelets for the Haar wavelet.[让我们创建的Haar小波的离散自相关小波。]
# We shall create up to scale 4.[我们将创建规模4。]
#[]
PsiJ(-4, filter.number=1, family="DaubExPhase")
#Computing PsiJ[计算PsiJ]
#Returning precomputed version[返回预先计算版本]
#Took 0.00999999 seconds[采取0.00999999秒]
#[[1]]:[[[1]]：]
#[1] -0.5 1.0 -0.5[[1] -0.5 1.0 -0.5]
#[]
#[[2]]:[[[2]]：]
#[1] -0.25 -0.50 0.25 1.00 0.25 -0.50 -0.25[[1] -0.25 -0.50 0.25 1.00 0.25 -0.50 -0.25]
#[]
#[[3]]:[[[3]]：]
# [1] -0.125 -0.250 -0.375 -0.500 -0.125 0.250 0.625 1.000 0.625 0.250[[1] -0.125 -0.250 -0.375 -0.500 -0.125 0.250 0.625 1.000 0.625 0.250]
#[11] -0.125 -0.500 -0.375 -0.250 -0.125[[11] -0.125 -0.500 -0.375 -0.250 -0.125]
#[]
#[[4]]:[[[4]]：]
# [1] -0.0625 -0.1250 -0.1875 -0.2500 -0.3125 -0.3750 -0.4375 -0.5000 -0.3125[[1] -0.0625 -0.1250 -0.1875 -0.2500 -0.3125 -0.3750 -0.4375 -0.5000 -0.3125]
#[10] -0.1250 0.0625 0.2500 0.4375 0.6250 0.8125 1.0000 0.8125 0.6250[[10] -0.1250 0.0625 0.2500 0.4375 0.6250 0.8125 1.0000 0.8125 0.6250]
#[19] 0.4375 0.2500 0.0625 -0.1250 -0.3125 -0.5000 -0.4375 -0.3750 -0.3125[[19] 0.4375 0.2500 0.0625 -0.1250 -0.3125 -0.5000 -0.4375 -0.3750 -0.3125]
#[28] -0.2500 -0.1875 -0.1250 -0.0625[[28] -0.2500 -0.1875 -0.1250 -0.0625]
#[]
# You can plot the fourth component to get an idea of what the[您可以绘制的第四个组件来获得一个想法是什么]
# autocorrelation wavelet looks like.[自相关小波的样子。]
#[]
# Note that the previous call stores the autocorrelation wavelet[需要注意的是前面的调用存储的自相关小波]
# in Psi.4.1.DaubExPhase. This is mainly so that it doesn't have to[在Psi.4.1.DaubExPhase。这主要是，因此，它并没有有]
# be recomputed. [重新计算。]
#[]
# Note that the x-coordinates in the following are approximate.[请注意，在下面的x坐标是近似的。]
#[]
## Not run: plot(seq(from=-1, to=1, length=length(Psi.4.1.DaubExPhase[[4]])),[＃不运行图（SEQ（= -1 = 1，长度=长度（Psi.4.1.DaubExPhase [[4]]）），]
Psi.4.1.DaubExPhase[[4]], type="l",
xlab = "t", ylab = "Haar Autocorrelation Wavelet")
## End(Not run)[＃（不执行）]
#[]
#[]
# Now let us repeat the above for the Daubechies Least-Asymmetric wavelet[现在，让我们再重复上述的最小不对称的Daubechies小波]
# with 10 vanishing moments.[10个消失的时刻。]
# We shall create up to scale 6, a higher resolution version than last[我们将创建规模，更高的分辨率版本，比去年]
# time.[时间。]
#[]
PsiJ(-6, filter.number=10, family="DaubLeAsymm", OPLENGTH=5000)
##[[1]]:[＃[[1]]：]
# [1] 3.537571e-07 5.699601e-16 -7.512135e-06 -7.705013e-15 7.662378e-05[[1] 3.537571e-07 5.699601e-16-7.512135e-06-7.705013e-15 7.662378e-05]
# [6] 5.637163e-14 -5.010016e-04 -2.419432e-13 2.368371e-03 9.976593e-13[[6] 5.637163e-14-5.010016e-04-2.419432e-13 2.368371e-03 9.976593e-13]
#[11] -8.684028e-03 -1.945435e-12 2.605208e-02 6.245832e-12 -6.773542e-02[[11]-8.684028e-03-1.945435e-12 2.605208e-02 6.245832e-12-6.773542e-02]
#[16] 4.704777e-12 1.693386e-01 2.011086e-10 -6.209080e-01 1.000000e+00[[16] 4.704777e-12 1.693386e-01 2.011086e-10-6.209080e-01 1.000000E +00]
#[21] -6.209080e-01 2.011086e-10 1.693386e-01 4.704777e-12 -6.773542e-02[[21]-6.209080e-01 2.011086e-10 1.693386e-01 4.704777e-12-6.773542e-02]
#[26] 6.245832e-12 2.605208e-02 -1.945435e-12 -8.684028e-03 9.976593e-13[[26] 6.245832e-12 2.605208e-02-1.945435e-12-8.684028e-03 9.976593e-13]
#[31] 2.368371e-03 -2.419432e-13 -5.010016e-04 5.637163e-14 7.662378e-05[[31] 2.368371e-03-2.419432e-13-5.010016e-04 5.637163e-14 7.662378e-05]
#[36] -7.705013e-15 -7.512135e-06 5.699601e-16 3.537571e-07[[36] 7.705013e-15-7.512135e-06 5.699601e-16 3.537571e-07]
#[]
#[[2]][[[2]]]
# scale 2 etc. etc.[规模2等等。]
#[]
#[[3]] scale 3 etc. etc.[[[3]]规模3等等。]
#[]
#scales [[4]] and [[5]]...[尺度[[4]]和[[5]] ...]
#[]
#[[6]][[[6]]]
#...[...]
# remaining scale 6 elements...[其余规模的6个元素...]
#...[...]
#[2371] -1.472225e-31 -1.176478e-31 -4.069848e-32 -2.932736e-41 6.855259e-33[[2371] 1.472225e-31-1.176478e-31-4.069848e-32-2.932736e-41 6.855259e-33]
#[2376] 5.540202e-33 2.286296e-33 1.164962e-42 -3.134088e-35 3.427783e-44[[2376] 5.540202e-33 2.286296e-33 1.164962e-42-3.134088e-35 3.427783e-44]
#[2381] -1.442993e-34 -2.480298e-44 5.325726e-35 9.346398e-45 -2.699644e-36[[2381] 1.442993e-34-2.480298e-44 5.325726e-35 9.346398e-45-2.699644e-36]
#[2386] -4.878634e-46 -4.489527e-36 -4.339365e-46 1.891864e-36 2.452556e-46[[2386] 4.878634e-46-4.489527e-36-4.339365e-46 1.891864e-36 2.452556e-46]
#[2391] -3.828924e-37 -4.268733e-47 4.161874e-38 3.157694e-48 -1.959885e-39[[2391] 3.828924e-37-4.268733e-47 4.161874e-38 3.157694e-48-1.959885e-39]
##[＃]
# Let's now plot the 6th component (6th scale, this is the finest[现在，让我们来绘制第六部分（第6规模，这是最好的]
# resolution, all the other scales will be coarser representations)[分辨率，所有其他的尺度将是粗糙的陈述）]
#[]
# Note that the previous call stores the autocorrelation wavelet[需要注意的是前面的调用存储的自相关小波]
# in Psi.6.10.DaubLeAsymm.[在Psi.6.10.DaubLeAsymm。]
#[]
# Note that the x-coordinates in the following are non-existant![请注意，在下面的x坐标是不存在的！]
#[]
## Not run: ts.plot(Psi.6.10.DaubExPhase[[6]], xlab = "t",[＃不运行：ts.plot（Psi.6.10.DaubExPhase [[6]]，xlab =“t”的，]
ylab = "Daubechies N=10 least-asymmetric Autocorrelation Wavelet")
## End(Not run)[＃（不执行）]
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 17:32:07
