LocalSpec.wd(wavethresh)
LocalSpec.wd()所属R语言包：wavethresh
Compute Nason and Silverman raw or smoothed wavelet periodogram.
计算利晨和Silverman原料或平滑的小波周期图。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This smoothing in this function is now obsolete. You should now use the function ewspec.
在这个函数是平滑现在已经过时。你现在应该使用的功能ewspec。
This function computes the Nason and Silverman raw or smoothed wavelet periodogram as described by Nason and Silverman (1995).
此函数计算的利晨和Silverman原料或平滑小波周期图利晨和Silverman（1995）所描述的。
用法----------Usage----------
## S3 method for class 'wd':
LocalSpec(wdS, lsmooth="none", nlsmooth=FALSE, prefilter=TRUE,
verbose=FALSE, lw.number=wdS$filter$filter.number,
lw.family=wdS$filter$family, nlw.number=wdS$filter$filter.number,
nlw.family=wdS$filter$family, nlw.policy="LSuniversal",
nlw.levels=0 nlevels(wdS) - 1), nlw.type="hard", nlw.by.level=FALSE,
nlw.value=0, nlw.dev=var, nlw.boundary=FALSE, nlw.verbose=FALSE,
nlw.cvtol=0.01, nlw.Q=0.05, nlw.alpha=0.05, nlw.transform=I,
nlw.inverse=I, debug.spectrum=FALSE, ...)
参数----------Arguments----------
参数：wdS
The stationary wavelet transform object that you want to smooth or square.
平稳小波变换的对象，你要平滑或方形。
参数：lsmooth
Controls the LINEAR smoothing. There are three options: "none", "Fourier" and "wavelet". They are described below. Note that Fourier begins with a capital "F".
控制的线性平滑。有三个选项：“无”，“傅立叶”和“小波”。他们被描述如下。注意傅立叶开始与一个大写字母“F”。
参数：nlsmooth
A switch to turn on (or off) the NONLINEAR wavelet shrinkage of (possibly LINEAR smoothed) local power coefficients. This option is either TRUE (to turn on the smoothing) or FALSE (to turn it off).
一个开关打开（或关闭）的非线性小波收缩的可能是线性平滑当地的电力系数。此选项是TRUE（打开平滑的）或FALSE（关闭）。
参数：prefilter
If TRUE then apply a prefilter to the actual stationary wavelet coefficients at each level. This is a low-pass filter that cuts off all frequencies above the highest frequency allowed by the (Littlewood-Paley) wavelet that bandpassed the current level coefficients. If FALSE then no prefilter is applied.
如果是TRUE，然后申请一个预过滤器的实际固定的小波系数在每个级别。这是一个低通滤波器，切断所有（的Littlewood-Paley）小波带通的电流电平的系数所允许的最高频率以上的频率。如果为FALSE，则没有预过滤器。
参数：verbose
If TRUE then the function chats about what it is doing. Otherwise it is silent.
如果是TRUE，那么在做什么，聊天的功能。否则，它是无声的。
参数：lw.number
If wavelet LINEAR smoothing is used then this option controls the filter number of the wavelet within the family used to perform the LINEAR wavelet smoothing.
如果使用的线性小波平滑，那么这个选项控制filter number在家庭中的小波用于执行线性小波光滑。
参数：lw.family
If wavelet LINEAR smoothing is used then this option controls the family of the wavelet used to perform the LINEAR wavelet smoothing.
如果使用小波线性平滑，那么这个选项控制family用于执行线性小波光滑小波。
参数：nlw.number
If NONLINEAR wavelet smoothing is also used then this option controls the filter number of the wavelet used to perform the wavelet shrinkage.
如果也使用非线性小波平滑，那么这个选项控制filter number用于执行小波收缩的小波。
参数：nlw.family
If NONLINEAR wavelet smoothing is also used then this option controls the family of the wavelet used to perform the wavelet shrinkage.
如果也使用非线性小波平滑，那么这个选项控制family用于执行小波收缩的小波。
参数：nlw.policy
If NONLINEAR wavelet smoothing is also used then this option controls the levels to use when performing wavelet shrinkage (see threshold.wd for different policy choices).
如果非线性小波光滑的，那么这个选项也可用于控制使用时的水平（见threshold.wd不同的政策选择）进行小波收缩。
参数：nlw.levels
If NONLINEAR wavelet smoothing is also used then this option controls the levels to use when performing wavelet shrinkage (see threshold.wd for a detailed description of how levels can be chosen).
如果非线性小波光滑的，那么这个选项也可用于控制使用时的水平（见threshold.wd详细描述了如何可以选择水平）进行小波收缩。
参数：nlw.type
If NONLINEAR wavelet smoothing is also used then this option controls the type of thresholding used in the wavelet shrinkage (either "hard" or "soft", but see threshold.wd for a list).
如果非线性小波光滑的，那么这个选项也可用于控制阈值的小波收缩（“硬”或“软”，但看到threshold.wd的列表）。
参数：nlw.by.level
If NONLINEAR wavelet smoothing is also used then this option controls whether level-by-level thresholding is used or if one threshold is chosen for all levels (see threshold.wd).
如果非线性小波光滑的，那么这个选项也可用于控制级阈值是否被使用或选择一个阈值，为各级（见threshold.wd“）。
参数：nlw.value
If NONLINEAR wavelet smoothing is also used then this option controls if a manual (or similar) policy is supplied to nlw.policy then the nlw.value option carries the manual threshold value (see threshold.wd).
也可用于非线性小波平滑，那么这个选项控制，如果手册（或类似）的政策是提供给nlw.policy然后nlw.value的选项进行手动阈值（见threshold.wd）。
参数：nlw.dev
If NONLINEAR wavelet smoothing is also used then this option controls the type of variance estimator that is used in wavelet shrinkages (see threshold.wd). One possibility is the Splus var() function, another is the WaveThresh function madmad().
如果也使用非线性小波平滑，那么这个选项控制方差估计的类型中使用的小波收缩率（见threshold.wd）。一种可能是S-PLUS（VAR）功能，另一种是WaveThresh的函数madmad（）。
参数：nlw.boundary
If NONLINEAR wavelet smoothing is also used then this option controls whether boundary coefficients are also thresholded (see threshold.wd).
如果非线性小波光滑的，那么这个选项也可以用来控制是否边界系数的阈值（见threshold.wd）。
参数：nlw.verbose
If NONLINEAR wavelet smoothing is also used then this option controls whether the threshold function prints out messages as it thresholds levels (see threshold.wd).
如果非线性小波光滑的，那么这个选项也可以用来控制是否在阈值函数打印出消息，因为它的阈值水平（见threshold.wd）。
参数：nlw.cvtol
If NONLINEAR wavelet smoothing is also used then this option controls the optimization tolerance is cross-validation wavelet shrinkage is used (see threshold.wd)
如果非线性小波光滑的，那么这个选项也可以用来控制优化公差是交叉验证小波收缩（见threshold.wd）
参数：nlw.Q
If NONLINEAR wavelet smoothing is also used then this option controls the Q value for wavelet shrinkage (see threshold.wd).
如果非线性小波光滑的，那么这个选项也可用于控制Q值的小波收缩（见threshold.wd“）。
参数：nlw.alpha
If NONLINEAR wavelet smoothing is also used then this option controls the alpha value for wavelet shrinkage (see threshold.wd).
如果非线性小波光滑的，那么这个选项也可用于控制的Alpha值小波收缩（见threshold.wd“）。
参数：nlw.transform
If NONLINEAR wavelet smoothing is also used then this option controls a transformation that is applied to the squared (and possibly linear smoothed) stationary wavelet coefficients before shrinkage. So, for examples, you might want to set nlw.transform=log to perform wavelet shrinkage on the logs of the squared (and possibly linear smoothed) stationary wavelet coefficients.
如果也使用非线性小波平滑，那么这个选项控制被施加到的平方（和可能的线性平滑）平稳小波系数收缩前的变换。因此，对于例子中，你可能需要设置nlw.transform=log的log（和可能的线性平滑）的平方平稳小波系数进行小波收缩。
参数：nlw.inverse
If NONLINEAR wavelet smoothing is also used then this option controls the inverse transformation that is applied to the wavelet shrunk coefficients before they are put back into the stationary wavelet transform structure. So, for examples, if the nlw.transform is log() you should set the inverse to nlw.inverse=exp.
如果也使用非线性小波平滑，那么这个选项控制被施加到的小波收缩系数的逆变换之前，他们被放回平稳小波变换结构。所以，举例来说，如果是nlw.transform log()，“你应该设置的逆nlw.inverse=exp。
参数：debug.spectrum
If this option is T then spectrum plots are produced at each stage of the squaring/smoothing. Therefore if you put in the non-decimated wavelet transform of white noise you can get a fair idea of how the coefficients are filtered at each stage.
如果此选项T然后频谱曲线是在每个阶段的平方/平滑。因此，如果你把在非抽取小波变换的白噪声，你可以得到如何在每个阶段的过滤系数是一个公平的想法。
参数：...
any other arguments
任何其他参数
Details
详细信息----------Details----------
This smoothing in this function is now obsolete. Use the function ewspec instead. However, this function is still useful for computing the raw periodogram.
在这个函数是平滑现在已经过时。使用功能ewspec。但是，此功能仍然是有用的计算原始的周期图。
This function attempts to produce a picture of local time-scale power of a signal. There are two main components to this function: linear smoothing of squared coefficients and non-linear smoothing of these. Neither, either or both of these components may be used to process the data. The function expects a non-decimated wavelet transform object (of class wd, type="station") such as that produced by the wd() function with the type option set to "station". The following paragraphs describe the various methods of smoothing.
该函数尝试的图片本地时间尺度的信号的功率。这功能：线性平滑系数的平方和非线性平滑这些有两个主要的组件。都不是，这些组件中的任一个或两个可能被用于对数据进行处理。函数需要一个非抽取小波变换对象（类WD，类型=“站”），如wd（）函数的类型“选项设置”station“产生的。下面的段落描述了各种方法的平滑。
LINEAR SMOOTHING. There are three varieties of linear smoothing. None simply squares the coefficients. Fourier and wavelet apply linear smoothing methods in accordance to the prescription given in Nason and Silverman (1995). Each level in the SWT corresponds to a band-pass filtering to a frequency range [sl, sh]. After squaring we obtain power in the range [0, 2sl] and [2sl, 2sh]. The linear smoothing gets rid of the power in [2sl, 2sh]. The Fourier method simply applies a discrete Fourier transform (rfft) and cuts off frequencies above 2sl. The wavelet method is a bit more suble. The DISCRETE wavelet transform is taken of a level (i) and all levels within the DWT, j, where j>i are set to zero and then the inverse is taken. Approximately this performs the same operation as the Fourier method only faster. By default the same wavelets are used to perform the linear smoothing as were used to compute the stationary wavelet transform in the first place. This can be changed by altering lw.number and lw.family.
线性平滑。有三个品种的线性平滑。没有简单的平方的系数。傅立叶和小波应用线性平滑在根据处方利晨和Silverman（1995）的方法。在SWT每个级别对应的带通滤波的频率范围[sl中，sh的]。后平方，我们获得权力的范围[0，2SL]和[2SL，2SH]。的线性平滑摆脱的的力量[2SL，2SH]。傅立叶方法只适用于离散傅立叶变换（rfft）和削减截止频率以上的2SL。小波方法多一点suble。离散小波变换的水平（i）及各级内的DWT，J，J>我设置为0，则反。约的Fourier方法不仅速度更快，执行相同的操作。缺省情况下，相同的小波被用于执行作为被用来计算平稳小波变换在首位的线性平滑。这是可以改变的，通过改变lw.number和lw.family。
NONLINEAR SMOOTHING. After either of the linear smoothing options above it is possible to use wavelet shrinkage upon each level in the squared (and possibly Fourier or wavelet linear smoothed) to denoise the coefficients. This process is akin to smoothing the ordinary periodogram. All the usual wavelet shrinkage options are available as nlw.* where * is one of the usual threshold.wd options. By default the same wavelets are used to perform the wavelet shrinkage as were used to compute the non-decimated wavelet transform. These wavelets can be replaced by altering nlw.number and nlw.family. Also, it is possible to transform the squared (and possibly smoothed coefficients) before applying wavelet shrinkage. The transformation is effected by supplying an appropriate transformation function (AND ITS INVERSE) to nlw.transform and nlw.inverse. (For examples, nlw.transform=log and nlw.inverse=exp might be a good idea).
非线性平滑。后的线性平滑以上选项之一是可以使用小波收缩中的每个电平的平方后（也可能是傅立叶变换或小波线性平滑化），去噪系数。这个过程类似于普通的周期图平滑。所有常用的小波收缩选项是可作为nlw*，其中*是一个通常的threshold.wd选项。缺省情况下，相同的小波被用于执行作为被用来计算非抽取小波变换的小波收缩。这些小波可以通过改变nlw.number和nlw.family取代。此外，它是可能的变换的平方（及可能平滑系数）之前施加小波收缩。通过提供适当的转换功能（及其逆定理）nlw.transform和nlw.inverse转型的影响。 （举例来说，nlw.transform=log和 nlw.inverse=exp可能是一个好主意）。
值----------Value----------
An object of class wd a time-ordered non-decimated wavelet transform. Each level of the returned object contains a smoothed wavelet periodogram. Note that this is not the corrected smoothed wavelet periodogram, or the evolutionary wavelet spectrum. Use the function ewspec to compute the evolutionary wavelet spectrum.
类的一个对象wd的时间有序的非抽取小波变换。每个级别返回的对象包含一个平滑的小波周期图。注意，这是不校正的平滑的小波周期图，或进化小波频谱。使用的功能ewspec计算的进化小波光谱。
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>,
实例----------Examples----------
#[]
# This function is obsolete. See ewspec()[这个函数是废弃的。见ewspec（）]
#[]
# Compute the raw periodogram of the BabyECG[计算的原始周期图的BabyECG]
# data using the Daubechies least-asymmetric wavelet $N=10$.[至少不对称的数据使用的Daubechies小波$ N = 10元。]
#[]
data(BabyECG)
babywdS <- wd(BabyECG, filter.number=10, family="DaubLeAsymm", type="station")
babyWP <- LocalSpec(babywdS, lsmooth = "none", nlsmooth = FALSE)
## Not run: plot(babyWP, main="Raw Wavelet Periodogram of Baby ECG")[＃不运行：的图（babyWP，主要=“RAW的小波周期图的婴儿心电图”）]
#[]
# Note that the lower levels of this plot are too large. This is partly because[需要注意的是图的较低水平，这是过大。这部分是因为]
# there are "too many" coefficients at the lower levels. For a better[有“太多”的系数在较低的水平。为了更好地]
# picture of the local spectral properties of this time series see[图片的地方对这种时间序列的频谱特性]
# the examples section of ewspec[的例子部分ewspec的]
#[]
# Other results of this function can be seen in the paper by[此功能的其他结果，可以看出，在纸]
# Nason and Silverman (1995) above.[利晨和Silverman（1995）以上。]
#[]
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。
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发表于 2012-10-1 17:24:35
