wst(wavethresh)
wst()所属R语言包：wavethresh
Packet-ordered non-decimated wavelet transform.
分组排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----------
Computes the packet-ordered non-decimated wavelet transform (TI-transform). This algorithm is functionally equivalent to the time-ordered non-decimated wavelet transform (computed by wd with the type="station" argument).
计算的分组有序的非抽取小波变换（TI变换）。此算法是功能等效的时间有序的非抽取小波变换（由wd与type="station"参数计算）。
用法----------Usage----------
wst(data, filter.number=10, family="DaubLeAsymm", verbose=FALSE)
参数----------Arguments----------
参数：data
A vector containing the data you wish to decompose. The length of this vector must be a power of 2.
一个向量，包含你想分解的数据。此向量的长度必须是2的幂的。
参数：filter.number
This selects the smoothness of wavelet that you want to use in the decomposition. By default this is 10, the Daubechies least-asymmetric orthonormal compactly supported wavelet with 10 vanishing moments.
选择要使用的分解小波的平滑度。默认情况下，这是10，至少不对称的Daubechies正交的紧支撑小波与10个消失矩。
参数：family
specifies the family of wavelets that you want to use. The options are "DaubExPhase" and "DaubLeAsymm".
指定要使用的小波家庭。的选项“DaubExPhase”和“DaubLeAsymm”。
参数：verbose
Controls the printing of "informative" messages whilst the computations progress. Such messages are generally annoying so it is turned off by default.
控制打印的“信息”的消息，而计算的进展。这样的消息一般都是讨厌的，所以它在默认情况下是关闭的。
Details
详细信息----------Details----------
The packet-ordered non-decimated wavelet transform is more properly known as the TI-transform described by Coifman and Donoho, 1995. A description of this implementation can be found in Nason and Silverman, 1995.
分组排序，TI变换所描述的Coifman和Donoho提出，1995年非抽取小波变换更为恰当。这个实现的描述中可以找到利晨和Silverman，1995年。
The coefficients produced by this transform are exactly the same as those produced by the wd function with the type="station" option except in that function the coefficients are time-ordered. In the wst function the coefficients are produced by a wavelet packet like algorithm with a cyclic rotation step instead of processing with the father wavelet mirror filter at each level.
通过此变换产生的系数由wd函数与type="station"选项产生的那些是完全相同的，除在该函数中的系数是时间排序。在wst函数的系数是由算法小波包像一个循环旋转，而不是处理与父亲小波镜像滤波器在每个级别。
The coefficients produced by this function are useful in curve estimation problems in conjunction with the thresholding function threshold.wst and either of the inversion functions AvBasis.wst and InvBasis.wst The coefficients produced by the time-ordered non-decimated wavelet transform are more useful for time series applications: e.g. the evolutionary wavelet spectrum computation performed by ewspec. Note that a time-ordered non-decimated wavelet transform object may be converted into a packet-ordered non-decimated wavelet transform object (and vice versa) by using the convert function.
此函数所产生的系数，是有用的，在结合的阈值函数threshold.wst和任一的反转功能AvBasis.wst和InvBasis.wst产生的系数由time-ordered non-decimated wavelet transform曲线估计问题更适用于时间序列的应用，例如：进化小波谱计算的ewspec。请注意，一个时间序的非抽取小波变换的对象可以被转换成convert函数通过使用分组命令非抽取小波变换对象（反之亦然）。
值----------Value----------
An object of class: wst. The help for the wst describes the intricate structure of this class of object.
对象类：wst。 wst的帮助说明这个类的对象的复杂结构。
RELEASE----------RELEASE----------
Version 3.5.3 Copyright Guy Nason 1995
版本3.5.3版权盖利晨1995年
（作者）----------Author(s)----------
G P Nason
参见----------See Also----------
wst.object, threshold.wst, AvBasis.wst, InvBasis.wst, filter.select, convert, ewspec, plot.wst,
wst.object，threshold.wst，AvBasis.wst，InvBasis.wst，filter.select，convert，ewspec，plot.wst，
实例----------Examples----------
#[]
# Let's look at the packet-ordered non-decimated wavelet transform[让我们来看看在分组排序的非抽取小波变换]
# of the data we used to do the time-ordered non-decimated wavelet[我们使用的数据做时间排序的非抽取小波]
# transform exhibited in the help page for wd. [展出的帮助页面WD的转变。]
#[]
test.data <- example.1()$y
#[]
# Plot it to see what it looks like (piecewise polynomial)[绘制它，看看它是什么样子（分段多项式）]
#[]
## Not run: ts.plot(test.data)[＃不运行：ts.plot（test.data）]
#[]
# Now let's do the packet-ordered non-decimated wavelet transform.[现在，让我们做分组排序的非抽取小波变换。]
#[]
TDwst <- wst(test.data)
#[]
# And let's plot it....[让我们绘制....]
#[]
## Not run: plot(TDwst)[＃不运行：图（TDwst）]
#[]
# The coefficients in this plot at each resolution level are the same[在这个图在每个分辨率级别的系数是相同的]
# as the ones in the non-decimated transform plot in the wd[在非锐减变换的图在WD的]
# help page except they are in a different order. For more information[帮助页面，除非他们是在不同的顺序。欲了解更多信息]
# about how the ordering works in each case see[有关如何在每一种情况下看到的顺序工程]
# Nason, Sapatinas and Sawczenko, 1998. [利晨，Sapatinas和Sawczenko的，1998年。]
# []
# Next examples[下面的例子]
# ------------[------------]
# The chirp signal is also another good examples to use.[线性调频信号是另一个很好的例子使用。]
#[]
#[]
# Generate some test data[生成一些测试数据。]
#[]
test.chirp <- simchirp()$y
## Not run: ts.plot(test.chirp, main="Simulated chirp signal")[＃不运行：ts.plot（test.chirp，主要=“模拟线性调频信号”）]
#[]
# Now let's do the packet-ordered non-decimated wavelet transform.[现在，让我们做分组排序的非抽取小波变换。]
# For a change let's use Daubechies extremal phase wavelet with 6[对于一个变化，让我们使用的Daubechies小波极值阶段6]
# vanishing moments (a totally arbitrary choice, please don't read[消失矩（完全任意选择，请不要阅读]
# anything into it).[任何东西进去）。]
#[]
chirpwst <- wst(test.chirp, filter.number=6, family="DaubExPhase")
## Not run: plot(chirpwst, main="POND WT of Chirp signal")[＃不运行：的图（chirpwst，主要=“POND的线性调频信号的WT”）]
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 20:23:11
