wstCVl(wavethresh)
wstCVl()所属R语言包：wavethresh
Performs two-fold cross-validation estimation using packet-ordered non-decimated wavelet transforms and a (vector) level-dependent threshold.
执行两折交叉验证估计，使用数据包排序，非抽取小波变换和水平依赖阈值（矢量）。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Performs Nason's 1996 two-fold cross-validation estimation using packet-ordered non-decimated wavelet transforms and a (vector) level-dependent threshold.
执行1996年的2倍利晨交叉验证估计，使用数据包排序，非抽取小波变换和水平依赖阈值（矢量）。
用法----------Usage----------
wstCVl(ndata, ll = 3, type = "soft", filter.number = 10, family = "DaubLeAsymm",
tol = 0.01, verbose = 0, plot.it = FALSE, norm = l2norm, InverseType = "average",
uvdev = madmad)
参数----------Arguments----------
参数：ndata
the noisy data. This is a vector containing the signal plus noise. The length of this vector should be a power of two.
噪声数据。这是一个向量，包含的信号加噪声。此向量的长度应该是二的幂的。
参数：ll
the primary resolution for this estimation. Note that the primary resolution is problem-specific: you have to find out which is the best value.
主要解决此估计。需要注意的是主要的分辨率是针对特定问题的：你必须找出哪些是最好的价值。
参数：type
whether to use hard or soft thresholding. See the explanation for this argument in the threshold.wst function.
是否要使用硬质或软质的阈值。这种说法的解释threshold.wst功能。
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：tol
the cross-validation tolerance which decides when an estimate is sufficiently close to the truth (or estimated to be so).
交叉验证性决定时的估计十分接近的真理（或估计）。
参数：verbose
If TRUE then informative messages are printed during the progression of the function, otherwise they are not.
如果TRUE的信息性消息时打印的功能的进展，否则他们是不会。
参数：plot.it
Whether or not to produce a plot indicating progress.
无论产生的图，表示进度。
参数：norm
which measure of distance to judge the dissimilarity between the estimates. The functions l2norm and linfnorm are suitable examples.
测量的距离来判断估计数之间的差异性。的功能l2norm和linfnorm是合适的例子。
参数：InverseType
The possible options are "average" or "minent". The former uses basis averaging to form estimates of the unknown function. The "minent" function selects a basis using the Coifman and Wickerhauser, 1992 algorithm to select a basis to invert.
可能的选项是“平均”或“minent的”。前者使用基础上平均形成的未知函数的估计。 “minent”功能选择使用Coifman和Wickerhauser的，1992年算法选择的基础反转的基础。
参数：uvdev
Universal thresholding is used to generate an upper bound for the ideal threshold. This argument provides the function that computes an estimate of the variance of the noise for use with the universal threshold calculation (see threshold.wst).
通用的阈值是用来产生了理想的阈值的上限。这一论点提供了函数，计算使用的通用阈值计算的噪声的方差的估计（见threshold.wst）。
Details
详细信息----------Details----------
This function implements a modified version of the cross-validation method detailed by Nason, 1996 for computing an estimate of the error between an estimate and the “truth”. The difference here is that it uses the packet ordered non-decimated wavelet transform rather than the standard Mallat wd discrete wavelet transform. As such it is an examples of the translation-invariant denoising of Coifman and Donoho, 1995 but uses cross-validation to choose the threshold rather than SUREshrink.
此功能实现了交叉验证方法计算的估计值之间的误差和“真理”的估计，1996年由利晨详细的修改后的版本。这里的区别是，它使用的数据包下令非抽取小波变换，而不是标准的Mallat的WD离散小波变换。因此，这是一个例子Coifman和Donoho提出，1995年的平移不变去噪的，但使用交叉验证来选择阈值，而不是SUREshrink。
Further, this function computes level-dependent thresholds. That is, it can compute a different threshold for each resolution level.
另外，这个函数的计算依赖于电平阈值。也就是说，它可以计算出一个不同的阈值的每个分辨率等级。
Note that the procedure outlined above can use AvBasis basis averaging or basis selection and inversion using the Coifman and Wickerhauser, 1992 best-basis algorithm
请注意，上述程序可以使用AvBasis基础上平均或基础选择和反演Coifman和Wickerhauser的，1992年最基础的算法
值----------Value----------
A list returning the results of the cross-validation algorithm. The list includes the following components:
列表A返回的交叉验证算法的结果。该列表包括以下组件：
<table summary="R valueblock"> <tr valign="top"><td>ndata</td> <td> a copy of the input noisy data</td></tr> <tr valign="top"><td>xvwr</td> <td> a reconstruction of the best estimate computed using this algorithm. It is the inverse (computed depending on what the InverseType argument was) of the xvwrWSTt component.</td></tr> <tr valign="top"><td>xvwrWSTt</td> <td> a thresholded version of the packet-ordered non-decimated wavelet transform of the noisy data using the best threshold discovered by this cross-validation algorithm.</td></tr> <tr valign="top"><td>uvt</td> <td> the universal threshold used as the upper bound for the algorithm that tries to discover the optimal cross-validation threshold. The lower bound is always zero.</td></tr> <tr valign="top"><td>xvthresh</td> <td> the best threshold as discovered by cross-validation. Note that this is vector, a level-dependent threshold with one threshold value for each resolution level. The first entry corresponds to level ll, the last entry corresponds to level nlevels(ndata)-1 and the entries in between linearly to the levels in between. The wstCV function should be used to compute a global threshold.</td></tr> <tr valign="top"><td>optres</td> <td> The results from performing the optimisation using the nlminb function from Splus. This object contains many interesting components with information about how the optimisation went. See the nlminb help page for information.</td></tr> </table>
<table summary="R valueblock"> <tr valign="top"> <TD> ndata</ TD> <TD>的输入噪声的数据的一个副本</ TD> </ TR> <TR VALIGN =“”> <TD>xvwr</ TD> <td>一个重建的最佳估计使用这种算法计算。这是逆（计算取决于上什么InverseType参数）xvwrWSTt组成部分。</ TD> </ TR> <tr valign="top"> <TD>xvwrWSTt</ TD <td>一个阈值版本的数据包排序，非抽取小波变换的噪声数据，使用交叉验证算法所发现的最佳阈值。</ TD> </ TR> <tr valign="top"> < uvt TD> </ TD> <TD>普遍使用的算法，试图找出最佳的交叉验证阈值的上限阈值。下限始终为零。</ TD> </ TR> <tr valign="top"> <TD>xvthresh </ TD> <TD>发现最佳阈值通过交叉验证。注意，这是向量，分辨率级别为每个阈值与一个阈值水平依赖。的第一个条目对应于水平ll，最后一个条目对应于水平nlevels(ndata)-1和的条目之间的水平之间的线性。 wstCV函数应该被用来计算一个全球性的阈值。</ TD> </ TR> <tr valign="top"> <TD> optres</ TD> <TD>的结果执行从S-PLUS使用nlminb功能的优化。这个对象包含了许多有趣的组件，如何优化信息。 nlminb帮助页的信息。</ TD> </ TR> </表>
RELEASE----------RELEASE----------
Version 3.6 Copyright Guy Nason 1995
3.6版版权盖利晨1995年
（作者）----------Author(s)----------
G P Nason
参见----------See Also----------
GetRSSWST, linfnorm, linfnorm, threshold.wst, wst, wst.object, wstCV
GetRSSWST，linfnorm，linfnorm，threshold.wst，wst，wst.object，wstCV
实例----------Examples----------
#[]
# Example PENDING[例如PENDING]
#[]
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 20:24:08
