wavelet.treethresh(treethresh)
wavelet.treethresh()所属R语言包：treethresh
Threshold wavelet coefficients
阈值的小波系数
译者：生物统计家园网 机器人LoveR
描述----------Description----------
wavelet.threshold is a more user-friendly function for thresholding wavelet coefficients stored in an wd or imwd object. It combines the functions extract.coefficients, estimate.sdev (rescales the coefficients accordingly), treethresh or wtthresh, prune, thresh, and insert.coefficients
wavelet.threshold阈值的小波系数存储在一个wd或imwd对象是一个更加用户友好的功能。它结合了功能extract.coefficients，estimate.sdev（重新调整相应的系数），treethresh或wtthresh，prune，thresh，<X >
用法----------Usage----------
wavelet.treethresh(object, sdev=NA, dev=mad, start.level=5,
levelwise=FALSE, v=5, sd.mult=0.5, postmed=TRUE, ...)
参数----------Arguments----------
参数：object
An object of the class wd or wtthresh to be smoothed by thresholding.
类的对象wd或wtthresh平滑阈值。
参数：sdev
The standard error of the noise (if known), otherwise NA.
的噪声标准的错误（如果知道的话），否则NA。
参数：dev
The function used to estimate the standard error. Typical examples are sd (teh default), or the more robust mad. Not used if sdev is given.
该函数用来估计的标准误差。典型的例子是sd（TEH默认），或更强大的mad。不sdev。
参数：start.level
The level in the wavelet transform from which to commence thresholding the wavelet coefficients. This level and all those finer levels will be thresholded.
在小波变换的电平开始阈值的小波系数。这将阈值水平，所有这些更精细的水平。
参数：levelwise
Indicates whether the thresholding should be carried out independently for each level and type of coefficients as in the function treethresh (if levelwise=TRUE), or whether a common partition is to be found jointly for all coefficients as in the function wtthresh (if levelwise=FALSE).
表示每个级别和类型的功能系数的阈值是否应进行独立treethresh（如果levelwise=TRUE），或是否是共同所有的系数被发现在一个共同的分区功能wtthresh（如果levelwise=FALSE“）。
参数：v
The number of folds in the cross-validation used to determine the optimal subtree in the pruning step (see the function prune for details). NA if no pruning is to be carried out.
倍交叉验证，以确定最佳的子树的修剪步骤（见函数prune）。 NA如果不进行修剪。
参数：sd.mult
The smallest subtree that is not sd.mult times the standard error worse than the best loglikelihood will be chosen as the optimal tree in the pruning step. (see the function prune for details).
将被选择作为最优树修剪步骤中最小的子树是不是sd.mult时间差的标准误差比最好loglikelihood。 （见的功能prune的详细信息）。
参数：postmed
Controls whether thresholding is to be carried out by using the posterior median of the coefficient mu|y (postmed=true) or by using a hard threshold (postmed=FALSE). (See the function thresh for more details.)
控制是否是通过使用后的中位数的系数进行阈值mu|y（postmed=true）或通过使用硬阈值（postmed=FALSE）。 （见的功能thresh更多的细节。）
参数：...
arguments passed to to wtthresh (if levelwise=FALSE) or treethresh (if levelwise=TRUE).
参数传递到wtthresh（如果levelwise=FALSE）treethresh（如果levelwise=TRUE）。
值----------Value----------
Returns an object of the class wd or wtthresh, where the coefficients have been thresholded using the TreeThresh algorithm.
返回一个对象的类wd或wtthresh，其中阈值的系数已经使用TreeThresh算法。
注意----------Note----------
wavelet.treethresh combines the functions extract.coefficients, estimate.sdev (and the appropriate scaling), treethresh / wtthresh, prune, insert.coefficients into a single (hopefully) more user-friendly function.
wavelet.treethresh的功能相结合extract.coefficients，estimate.sdev（和适当的缩放），treethresh/wtthresh，prune，insert.coefficients成一个单一的（希望）更多的用户友好功能。
参考文献----------References----------
Evers, L. and Heaton, T. (2009) Locally Adaptive Tree-Based Thresholding. Journal of Computational and Graphical Statistics. Dec 2009, Vol. 18, No. 4: 961-977.
参见----------See Also----------
extract.coefficients, estimate.sdev, treethresh, wtthresh, prune, thresh, insert.coefficients
extract.coefficients，estimate.sdev，treethresh，wtthresh，prune，thresh，insert.coefficients
实例----------Examples----------
## The following examples shows how an example image can be[＃以下示例显示了如何可以是一个例子形象]
## thresholded using the more user-friendly function[＃阈值使用更多的用户友好功能]
## wavelet.treethresh[＃wavelet.treethresh]
## (01) Load the example image[＃（01）负载的例子图像]
data(tiles)
## (02) Display the image[＃（02）显示图像]
par(mai=rep(0,4)) ; image(tiles,col=grey(0:255/255))
## (03) Add noise to the image[＃（03）添加噪声的图像]
corrupted <- tiles + rnorm(length(tiles))
## (04) Display the corrupted image[＃（04）显示损坏的图像]
par(mai=rep(0,4)) ; image(corrupted,col=grey(0:255/255))
## (05) Compute the wavelet transform[＃（05）计算的小波变换]
corrupted.wt <- imwd(corrupted)
## (06) Perform the thresholding[＃（06）执行阈值]
denoised.wt <- wavelet.treethresh(corrupted.wt)
## (07) Compute inverse wavelet transform[＃（07）计算小波逆变换]
denoised <- imwr(denoised.wt)
## (08) Display denoised image[＃（08）显示图像去噪]
par(mai=rep(0,4)) ; image(denoised,col=grey(0:255/255))
## (09) Compute l2 loss[＃（09）计算L2损失]
sum((denoised-tiles)^2)
## The call to wavelet.treethresh is equivalent to steps (06) to (13)[＃调用到wavelet.treethresh是相当于步骤（06）至（13）]
## of the example in the help section "coefficients".[在帮助部分“系数”＃的例子。]
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。
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发表于 2012-10-1 11:54:04
