CWCV(wavethresh)
CWCV()所属R语言包：wavethresh
C Wavelet Cross-validation
Ç小波交叉验证
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Two-fold wavelet shrinkage cross-validation (in C)
两方面的小波收缩交叉验证（C语言）
用法----------Usage----------
CWCV(ynoise, ll, x = 1:length(ynoise), filter.number = 10, family =
"DaubLeAsymm", thresh.type = "soft", tol = 0.01, verbose = 0,
plot.it = TRUE, interptype = "normal")
参数----------Arguments----------
参数：ynoise
A vector of dyadic (power of two) length that contains the noisy data that you wish to apply wavelet shrinkage by cross-validation to.
矢（幂）的矢量长度包含噪声的数据，你想申请小波收缩交叉验证。
参数：ll
The primary resolution that you wish to assume. No wavelet coefficients that are on coarser scales than ll will be thresholded.
你愿意承担的主要决议。没有将阈值的小波系数是粗糙的尺度比LL。
参数：x
This function is capable of producing informative plots. It can be useful to supply the x values corresponding to the ynoise values. Further this argument is returned by this function which can be useful for later processors.
此功能是能够生产的信息图。对应的ynoise值的x值，它可以是有用供货。此外，这种说法是返回的功能，可用于以后的处理器。
参数：filter.number
This selects the smoothness of wavelet that you want to perform wavelet shrinkage by cross-validation.
这将选择要通过交叉验证进行小波收缩小波的平滑度。
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：thresh.type
this option specifies the thresholding type which can be "hard" or "soft".
此选项指定的阈值类型，可以是“硬”或“软”。
参数：tol
this specifies the convergence tolerance for the cross-validation optimization routine (a golden section search).
指定交叉验证优化程序（黄金分割搜索）的收敛公差。
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
参数：plot.it
If this is TRUE then plots of the universal threshold (used to obtain an upper bound on the cross-validation threshold) reconstruction and the resulting cross-validation estimate are produced.
如果这是真的，然后绘制的通用阈值（用来获取上的交叉验证阈值的上限）重建和由此产生的交叉验证估计。
参数：interptype
Can take two values noise or normal. This option controls how cross-validation compares the estimate formed by leaving out the data with the "left-out" data. If interptype="noise" then two noisy values are averaged to compare with the estimated curve in between, otherwise if interptype="normal" then the curve estimate is averaged either side of a noisy left-out point.
可以取两个值的噪声或正常。该选项控制交叉验证比较所形成的“左”的数据留出的数据估计。如果interptype =“噪声”然后两个嘈杂的值的平均值，以比较的估计曲线之间，否则如果interptype =“正常”，然后曲线估计平均嘈杂的左侧出点两侧。
Details
详细信息----------Details----------
Compute the two-fold cross-validated wavelet shrunk estimate given the noisy data ynoise according to the description given in Nason, 1996.
计算缩水的2倍交叉验证小波估计喧闹的数据ynoise根据1996年在利晨给出的描述。
You must specify a primary resolution given by ll. This must be specified individually on each data set and can itself be estimated using cross-validation (although I haven't written the code to do this).
您必须指定一个主要的ll决议案。必须单独指定对每个数据集本身就可以使用交叉验证估计（虽然我不写代码，做到这一点）。
Note. The two-fold cross-validation method performs very badly if the input data is correlated. In this case I would advise using the methods proposed in Donoho and Johnstone, 1995 or Johnstone and Silverman, 1997 which can be carried out in WaveThresh using the threshold function using the policy="sure" option.
请注意。如果输入的数据是相关的2倍交叉验证方法进行得很厉害。在这种情况下，我会奉劝在使用threshold函数使用policy="sure"选项的使用方法提出了Donoho和Johnstone，1995年或约翰斯通和Silverman，1997年进行了WaveThresh。
值----------Value----------
A list with the following components
以下组件列表
<table summary="R valueblock"> <tr valign="top"><td>x</td> <td> This is just the x that was input. It gets passed through more or less for convenience for the user.</td></tr> <tr valign="top"><td>ynoise</td> <td> A copy of the input ynoise noisy data.</td></tr> <tr valign="top"><td>xvwr</td> <td> The cross-validated wavelet shrunk estimate.</td></tr> <tr valign="top"><td>yuvtwr</td> <td> The universal thresholded version (note this is merely a starting point for the cross-validation algorithm. It should not be ta ken seriously as an estimate. In particular its estimate of variance is likely to be inflated.) </td></tr> <tr valign="top"><td>xvthresh</td> <td> The cross-validated threshold</td></tr> <tr valign="top"><td>xvdof</td> <td> The number of non-zero coefficients in the cross-validated shrunk wavelet object (which is not returned).</td></tr> <tr valign="top"><td>uvdof</td> <td> The number of non-zero coefficients in the universal threshold shrunk wavelet object (which also is not returned)</td></tr> <tr valign="top"><td>xkeep</td> <td> always returns NULL!</td></tr> <tr valign="top"><td>fkeep</td> <td> always returns NULL!</td></tr>
<table summary="R valueblock"> <tr valign="top"> <TD>x</ TD> <td>这仅仅是x的输入。它被传递更多或更少的方便的用户。</ TD> </ TR> <tr valign="top"> <TD> ynoise </ TD> <td>一个副本输入ynoise噪声数据。</ TD> </ TR> <tr valign="top"> <TD>xvwr </ TD> <TD>交叉验证小波萎缩的估计。</ TD> </ TR> <tr valign="top"> <TD> yuvtwr </ TD> <TD>通用阈值的版本（请注意，这仅仅是一个起点交叉验证算法。它不应该是达根严重的估计，特别是其方差的估计是可能被夸大了。）</ TD> </ TR> <tr valign="top"> <TD> xvthresh </ td> <td>使用交叉 - 验证阈值</ TD> </ TR> <tr valign="top"> <TD>xvdof </ TD> <TD>非零系数的交叉验证缩水小波对象的数量（即不退还）。</ TD> </ TR> <tr valign="top"> <TD>uvdof </ TD> <TD>的数量非零系数的通用阈值萎缩小波对象（这也不会返回）</ TD> </ TR> <tr valign="top"> <TD>xkeep </ TD> <TD>总是返回NULL！</ TD> </ TR> <tr valign="top"> <TD>fkeep </ TD> <TD>总是返回NULL！</ TD> </ TR>
</table>
</ TABLE>
RELEASE----------RELEASE----------
Version 3.0 Copyright Guy Nason 1994
3.0版版权盖利晨1994年
注意----------Note----------
Plots of the universal and cross-validated shrunk estimates might be plotted if plot.it=TRUE.
图的普及和交叉验证的缩水估计可能会绘制如果plot.it=TRUE.
（作者）----------Author(s)----------
G P Nason
参见----------See Also----------
threshold. threshold.wd.
threshold。 threshold.wd。
实例----------Examples----------
#[]
# This function is best used via the policy="cv" option in[此功能是最好的政策，通过使用“CV”选项，]
# the threshold.wd function.[功能的threshold.wd。]
# See examples there.[参见例子。]
#[]
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。
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发表于 2012-10-1 17:16:59
