ewspec(wavethresh)
ewspec()所属R语言包：wavethresh
Compute evolutionary wavelet spectrum estimate.
计算进化小波谱估计。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function computes the evolutionary wavelet spectrum (EWS) estimate from a time series (or non-decimated wavelet transform of a time series). The estimate is computed by taking the non-decimated wavelet transform of the time series data, taking its modulus; smoothing using TI-wavelet shrinkage and then correction for the redundancy caused by use of the non-decimated wavelet transform. Options below beginning with smooth. are passed directly to the TI-wavelet shrinkage routines.
此函数计算的进化小波功率谱（EWS）估计，从时间序列（非抽取小波变换的时间序列）。计算估计是通过服用非抽取小波变换的时间序列数据，以它的弹性模量;平滑使用TI小波的收缩，然后校正所造成的非抽取小波变换使用的冗余。下面的选项开始与光滑。直接传递到TI-小波收缩例程。
用法----------Usage----------
ewspec(x, filter.number = 10, family = "DaubLeAsymm",
UseLocalSpec = TRUE, DoSWT = TRUE, WPsmooth = TRUE, verbose = FALSE,
smooth.filter.number = 10, smooth.family = "DaubLeAsymm", smooth.levels = 3 nlevels(WPwst) - 1), smooth.dev = madmad, smooth.policy =
"LSuniversal", smooth.value = 0, smooth.by.level = FALSE, smooth.type = "soft", smooth.verbose = FALSE, smooth.cvtol = 0.01, smooth.cvnorm = l2norm, smooth.transform = I, smooth.inverse = I)
参数----------Arguments----------
参数：x
The time series that you want to analyze. (See DETAILS below on how to supply preprocessed versions of the time series which bypass early parts of the ewspec function).
你要分析的时间序列。 （详见下文上如何提供时间序列的预处理版本，绕过早期部分的ewspec功能）。
参数：filter.number
This selects the index of the wavelet used in the analysis of the time series (i.e. the wavelet basis functions used to model the time series). For Daubechies compactly supported wavelets the filter number is the number of vanishing moments.
当前选择的索引中使用的时间序列的分析（即小波基函数用来模拟的时间序列）的小波。的Daubechies紧支撑小波消失矩的过滤器号码是多少。
参数：family
This selects the wavelet family to use in the analysis of the time series (i.e. which wavelet family to use to model the time series). Only use the Daubechies compactly supported wavelets DaubExPhase and DaubLeAsymm.
这选择小波家庭中使用的时间序列的分析（即小波族使用时间序列建模）。只有使用的Daubechies紧支撑小波DaubExPhase和DaubLeAsymm。
参数：UseLocalSpec
If you input a time series for x then this argument should always be T. (However, you can precompute the modulus of the non-decimated wavelet transform yourself and supply it as x in which case the LocalSpec call within this function is not necessary and you can set UseLocalSpec equal to F).
如果您输入的时间序列x那么这种说法应该始终是T。 （不过，你可以预先计算的模量，非抽取小波变换自己和供应x在这种情况下，LocalSpec呼吁在这个函数中是没有必要的，你可以设置UseLocalSpec等于F）。
参数：DoSWT
If you input a time series for x then this argument should always be T. (However, you can precompute the non-decimated wavelet transform yourself and supply it as x in which case the wd call within the function will not be necessary and you can set DoSWT equal to F).
如果您输入的时间序列x那么这种说法应该始终是T。 （不过，你可以预先计算非抽取小波变换，并提供其作为x在这种情况下，wd内部呼叫的功能是必要的，，你可以设置从平等到DoSWTF ）。
参数：WPsmooth
Normally a wavelet periodogram is smoothed before it is corrected. Use WPsmooth=F is you do not want any wavelet periodogram smoothing (correction is still done).
通常情况下，小波变换的周期图平滑之前被校正。使用WPsmooth=F你不希望任何的小波周期图平滑（仍然完成校正）。
参数：verbose
If this option is T then informative messages are printed as the function progresses.
如果此选项T然后信息会输出的功能进行。
参数：smooth.filter.number
This selects the index number of the wavelet that smooths each scale of the wavelet periodogram. See filter.select for further details on which wavelets you can use. Generally speaking it is a good idea to use a smoother wavelet for smoothing than the one you used for analysis (above) but since one still wants local smoothing it is best not to use a wavelet that is much smoother.
这将选择的小波平滑各尺度的小波周期图的索引号。见filter.select的进一步详情的小波，你可以使用。一般来说，它是一个好主意，使用更顺畅的小波比你使用的分析（上图），但因为人们仍然希望当地平滑，最好不要使用小波更加流畅平滑。
参数：smooth.family
This selects the wavelet family that smooths each scale of the wavelet periodogram. See filter.select for further details on which wavelets you can use. There is no need to use the same family as you used to analyse the time series.
这将选择的小波族平滑各尺度的小波周期图。见filter.select的进一步详情的小波，你可以使用。有没有必要使用相同的家庭作为：用于分析的时间序列。
参数：smooth.levels
The levels to smooth when performing the TI-wavelet shrinkage smoothing.
时的水平，顺利执行TI-小波收缩平滑。
参数：smooth.dev
The method for estimating the variance of the empirical wavelet coefficients for smoothing purposes.
用于估计的方差的实证小波系数，用于平滑的目的的方法。
参数：smooth.policy
The recipe for smoothing: determines how the threshold is chosen. See threshold for TI-smoothing and choice of potential policies. For EWS estimation LSuniversal is recommended for thi Chi-squared nature of the periodogram coefficients. However, if the coefficients are transformed (using smooth.transform and smooth.inverse) then other, more standard, policies may be appropriate.
平滑的配方：确定如何选择阈值。见thresholdTI平滑和选择潜在的政策。 EWS估计LSuniversal推荐氏卡方性质的周期图系数。但是，如果转化系数（使用smooth.transform和smooth.inverse），标准，政策可能是合适的。
参数：smooth.value
When a manual policy is being used this argument is used to supply a threshold value. See threshold for more information.
当手工政策正在使用这个参数是用来提供一个阈值。见threshold更多信息。
参数：smooth.by.level
If TRUE then the wavelet shrinkage is performed by computing and applying a separate threshold to each level in the non-decimated wavelet transform of each scale. Note that each scale in the EWS is smoothed separately and independently: and each smooth consists of taking the (second-stage) non-decimated wavelet transform and applying a threshold to each level of a wavelet transformed scale.
如果TRUE然后通过计算和应用一个单独的阈值中的每个级别的非抽取小波变换各尺度进行小波收缩。请注意，每个规模在EWS平滑分开和独立：各平滑由服用（第二阶段）非抽取小波变换，并施加一个阈值，每一级的小波变换的规模。
If FALSE then the same threshold is applied to the non-decimated wavelet transform of a scale. Different thresholds may be computed for different scales (in the time series model) but the threshold will be the same for each level arising from the non-decimated transform of a scale.
如果FALSE然后相同的阈值被施加到非抽取小波变换的一个尺度。不同的阈值，可针对不同的尺度计算（在时间序列模型），但为每个电平而产生的非抽取变换的规模的阈值将是相同的。
Note: a scale refers to a set of coefficients coming from a particular scale of the non-decimated wavelet transform of the time series data that models the time series. A level refers to the levels of wavelet coefficients obtained from taking the non-decimated wavelet transform of a particular scale.
注：scale是指来自一个特定规模的非抽取小波变换的时间序列数据，models的时间序列的一组系数。 Alevel是指采取一个特定的范围内的非抽取小波变换获得的小波系数的水平。
参数：smooth.type
The type of shrinkage: either "hard" or "soft".
收缩的类型：或者“硬”或“软”。
参数：smooth.verbose
If T then informative messages concerning the TI-transform wavelet shrinkage are printed.
如果T然后翔实的信息有关TI变换小波收缩打印。
参数：smooth.cvtol
If cross-validated wavelet shrinkage (smooth.policy="cv") is used then this argument supplies the cross-validation tolerance.
如果交叉验证的小波收缩（smooth.policy="cv"）被使用，那么这种说法提供了交叉验证的耐受性。
参数：smooth.cvnorm
no description for object
没有描述的对象
参数：smooth.transform
The transform function to use to transform the wavelet periodogram estimate. The wavelet periodogram coefficients are typically chi-squared in nature, a log transform can pull the coefficients towards normality so that a smooth.policy for Gaussian data could be used (e.g. universal).
转换函数使用，改造的小波周期图估计。的小波周期图系数是典型的卡方性质，一个log变换可以拉动系数对正常，一个smooth.policy高斯数据可以使用（例如universal）。
参数：smooth.inverse
the inverse transform of smooth.transform.
的逆变换smooth.transform。
Details
详细信息----------Details----------
This function computes an estimate of the evolutionary wavelet spectrum of a time series according to the paper by Nason, von Sachs and Kroisandt. The function works as follows:
此函数计算根据利晨，冯·高盛和Kroisandt的的纸的时间序列的进化小波频谱的估计。该功能的工作原理如下：
1The non-decimated wavelet transform of the series is computed.
（1）非抽取小波变换的一系列计算。
2The squared modulus of the non-decimated wavelet transform is computed (this is the raw wavelet periodogram, which is returned).
2The模量的平方非抽取小波变换计算（这是原始的的的小波周期图，然后将其返回）。
3The squared modulus is smoothed using TI-wavelet shrinkage.
使用TI-小波收缩平滑3The平方模量。
4The smoothed coefficients are corrected using the inverse of the inner product matrix of the discrete non-decimated autocorrelation wavelets (produced using the ipndacw function).
（4）平滑化系数进行校正使用的离散非抽取的自相关小波（生产使用ipndacw函数）的内积矩阵的逆。
To display the EWS use the plotfunction on the S component, see the examples below.
要显示EWS使用plot的功能S组件的信息，请参阅下面的例子。
It is possible to supply the non-decimated wavelet transform of the time series and set DoSWT=F or to supply the squared modulus of the non-decimated wavelet transform using LocalSpec and setting UseLocalSpec=F. This facility saves time because the function is then only used for smoothing and correction.
这是可能的，以提供非抽取小波变换的时间序列，设定DoSWT=F或供货的平方模量的非抽取小波变换使用LocalSpec和设置UseLocalSpec=F。这家工厂节省时间，因为该功能仅用于平滑和修正。
值----------Value----------
A list with the following components:
以下组件列表：
<table summary="R valueblock"> <tr valign="top"><td>S</td> <td> The evolutionary wavelet spectral estimate of the input x. This object is of class wd and so can be plotted, printed in the usual way. </td></tr> <tr valign="top"><td>WavPer</td> <td> The raw wavelet periodogram of the input x. The EWS estimate (above) is the smoothed corrected version of the wavelet periodgram. The wavelet periodogram is of class wd and so can be plotted, printed in the usual way. </td></tr> <tr valign="top"><td>rm</td> <td> This is the matrix A from the paper by Nason, von Sachs and Kroisandt. Its inverse is used to correct the raw wavelet periodogram. This matrix is computed using the ipndacw function. </td></tr> <tr valign="top"><td>irm</td> <td> The inverse of the matrix A from the paper by Nason, von Sachs and Kroisandt. It is used to correct the raw wavelet periodogram.</td></tr> </table>
<table summary="R valueblock"> <tr valign="top"> <TD> S</ TD> <TD>进化小波谱估计的输入x。对象是类wd“等，可以绘制，印刷通常的方式。 </ TD> </ TR> <tr valign="top"> <TD> WavPer</ TD> <TD>原始的的小波周期图输入x。的的EWS估计（见上文）的平滑矫正版本的小波periodgram的。小波周期图是类wd“等，可以绘制，印刷中常用的方法。 </ TD> </ TR> <tr valign="top"> <TD>rm </ TD> <TD>利晨，冯高盛和Kroisandt的，这是矩阵A的文件。其逆用于，纠正原料小波周期图。这个矩阵是计算ipndacw功能的。 </ TD> </ TR> <tr valign="top"> <TD> irm</ TD> <TD>的逆矩阵A的利晨，冯·高盛和Kroisandt的的纸。它被用于校正的原始的小波周期图。</ TD> </ TR> </表>
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>Baby Data</code>, <code>filter.select</code>, <code>ipndacw</code>, <code>LocalSpec</code>, <code>threshold</code> <code>wd</code> <code>wd.object</code>
实例----------Examples----------
#[]
# Apply the EWS estimate function to the baby data[EWS估计功能给宝宝数据]
#[]
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 17:19:17
