wstCV(wavethresh)
wstCV()所属R语言包：wavethresh
Performs two-fold cross-validation estimation using packet-ordered non-decimated wavelet transforms and one, global, threshold.
执行使用数据包排序，非抽取小波变换和一，全球，阈值2倍交叉验证估计。
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 one, global, threshold.
执行1996年的2倍利晨交叉验证估计，使用数据包排序，非抽取小波变换和一，全球，阈值。
用法----------Usage----------
wstCV(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
If TRUE then a plot of the progress of optimising the error estimate for different values of the threshold is generated as the algorithm proceeds. The algorithm tries to minimize the error estimate so you should see a “bowl” developing. After each iteration the error estimate is plotted with the iteration number so you should see the numbers tend to the bottom of the bowl.
如果TRUE然后产生一个图的进展，优化不同的阈值值的误差估计算法进行。该算法尝试，以尽量减少误差估计，所以你应该看到一个“碗”的发展。每次迭代后的误差估计迭代次数的曲线，所以你应该看到的数字往往碗的底部。
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 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的方法，详细的利晨，1996年计算的估计值之间的误差和“真理”的估计。这里的区别是，它使用了packet ordered non-decimated wavelet transform，而不是标准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。
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 one number, the global threshold. The wstCVl function should be used to compute a level-dependent threshold. </td></tr> <tr valign="top"><td>xkeep</td> <td> a vector containing the various thresholds used by the optimisation algorithm in trying to determine the best one. The length of this vector cannot be pre-determined but depends on the noisy data, thresholding method, and optimisation tolerance. </td></tr> <tr valign="top"><td>fkeep</td> <td> a vector containing the value of the estimated error used by the optimisation algorithm in trying to minimize the estimated error. The length, like that of xkeep cannot be predetermined for the same reasons.</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xvwrWSTt组件的倒数（计算的取决于上什么InverseType参数的）。 </ TD> </ TR> <tr valign="top"> <TD>xvwrWSTt </ TD> <td>一个阈值版本的分组排序的非抽取小波变换的噪声数据，使用发现交叉验证算法的最佳阈值。</ TD> </ TR> <tr valign="top"> <TD>uvt</ TD> <TD>的全局阈值作为上界试图找出最佳的交叉验证阈值的算法。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，这是一个数字，全局阈值。 wstCVl函数应该被用来计算一个阈值水平依赖。 </ TD> </ TR> <tr valign="top"> <TD>xkeep</ TD> <td>一个向量用不同的阈值的优化算法，以确定最佳的一个。这个向量的长度不能预先确定，但依赖于喧闹的数据，阈值法，和优化能力。 </ TD> </ TR> <tr valign="top"> <TD> fkeep</ TD> <td>一个向量，在尝试使用优化算法的估计误差值最小化估计误差。的长度，如不能预先确定的xkeep出于同样的原因。</ TD> </ TR> </ TABLE>
RELEASE----------RELEASE----------
注意----------Note----------
If plot.it is TRUE then a plot indicating the progression of the optimisation algorithm is plotted.
如果plot.itTRUE的图进展的优化算法绘制。
（作者）----------Author(s)----------
G P Nason
参见----------See Also----------
GetRSSWST, linfnorm, linfnorm, threshold.wst, wst, wst.object, wstCVl.
GetRSSWST，linfnorm，linfnorm，threshold.wst，wst，wst.object，wstCVl。
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 20:23:54
