threshold.mwd(wavethresh)
threshold.mwd()所属R语言包：wavethresh
Use threshold on an mwd object.
使用MWD对象的阈值。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Applies hard or soft thresholding to multiple wavelet decomposition object mwd.object.
适用于硬质或软阈值到多小波分解对象mwd.object的的。
用法----------Usage----------
## S3 method for class 'mwd':
threshold(mwd, levels = 3 nlevels(mwd) - 1), type = "hard",
policy = "universal", boundary = FALSE, verbose = FALSE,
return.threshold = FALSE, threshold = 0, covtol = 1e-09,
robust = TRUE, return.chisq = FALSE,
bivariate = TRUE, ...)
参数----------Arguments----------
参数：mwd
The multiple wavelet decomposition object that you wish to threshold.
多小波分解的对象，你想的阈值。
参数：levels
a vector of integers which determines which scale levels are thresholded in the decomposition. Each integer in the vector must refer to a valid level in the mwd object supplied. This is usually any integer from 0 to nlevels(wd)-1 inclusive. Only the levels in this vector contribute to the computation of the threshold and its application.
决定哪些规模水平的阈值分解的向量整数。向量中的每个整数必须在mwd对象提供一个有效的水平。这通常是从0到nlevels（WD）-1包容性的任一整数。只有在此向量的水平作出贡献的阈值的计算及其应用。
参数：type
determines the type of thresholding this can be "hard" or "soft".
确定阈值的类型，这可能是“hard”或“soft”。
参数：policy
selects the technique by which the threshold value is selected. Each policy corresponds to a method in the literature. At present the different policies are "universal", "manual", "single". The policies are described in detail below.
选择被选择的阈值的技术，通过该技术。每个策略对应的方法在文献中。目前，不同的政策是“universal”，“manual”，“single”。在下面详细描述的政策。
参数：boundary
If this argument is TRUE then the boundary bookeeping values are included for thresholding, otherwise they are not.
如果这种说法是TRUE然后的边界bookeeping值，包括阈值，否则他们是不会。
参数：verbose
if TRUE then the function prints out informative messages as it progresses.
如果TRUE那么该函数打印出来的信息性消息，因为它的进展。
参数：return.threshold
If this option is TRUE then the actual value of the threshold is returned. If this option is FALSE then a thresholded version of the input is returned.
如果此选项TRUE然后实际的阈值，则返回。如果此选项为FALSE，则返回一个版本的输入阈值。
参数：threshold
This argument conveys the user supplied threshold. If the policy="manual" then value is the actual threshold value. Any other policy means that the threshold value is ignored.
这个参数传递的用户提供的阈值。如果policy="manual"然后value是实际的阈值。任何其他policy是指threshold这个值将被忽略。
参数：covtol
The tolerance for what constitutes a singular variance matrix. If smallest eigenvalue of the estimated variance matrix is less than covtol then it is assumed to be singular and no thresholding is done at that level. Note: do not confuse covtol with cvtol an argument in threshold.wd.
是什么构成了奇异方差矩阵的耐受性。最小的估计方差矩阵的特征值是小于covtol然后它被认为是奇异的，并没有阈值是在该级别。注意：不要混淆covtolcvtol中的参数threshold.wd。
参数：robust
If TRUE the variance matrix at each level is estimated using a robust method (mad) otherwise it is estimated using var().
如果为true，在每个级别的协方差矩阵估计使用一种稳健的方法（MAD），否则估计使用var（）。
参数：return.chisq
If TRUE the vector of values to be thresholded is returned. These values are a quadratic form of each coefficient vector, and under normal assumptions the noise component will have a chi-squared distribution (see Downie and Silverman 1996).
如果矢量的值进行阈值，则返回TRUE。这些值是每个系数向量，二次型，在正常的假设的噪声成分将有一个卡方分布（见唐尼和Silverman，1996）。
参数：bivariate
this line is in construction
这条线正在建设中
参数：...
any other arguments
任何其他参数
Details
详细信息----------Details----------
Thresholding modifies the coefficients within a mwd.object. The modification can be performed either with a "hard" or "soft" thresholding selected by the type argument.
阈值修改系数在mwd.object。修改就可以进行“硬”或“软”阈值类型参数的选择。
Unless policy="single", the following method is applied. The columns of mwd$D are taken as coefficient vectors D_{j,k}. From these \chi^2_{j,k}=D_{j,k} \cdot V_j^{-1}. D_{j,k} is computed, where V_j^{-1} is the inverse of the estimated variance of the coefficient vectors in that level (j). \chi^2_{j,k} is a positive scalar which is to be thresholded in a similar manner to univariate hard or soft thresholding. To obtain the new values of D_{j,k} shrink the vector by the same proportion as was the corresponding \chi^2_{j,k} term. i
除非政策“单一”，下面的方法。列mwd$D系数向量D_{j,k}。从这些\chi^2_{j,k}=D_{j,k} \cdot V_j^{-1}。 D_{j,k}计算，其中V_j^{-1}是估计的方差的倒数，在该级别的系数向量（j）条。 \chi^2_{j,k}是一个正的标量，它是要以类似的方式单变量的硬质或软质的阈值的阈值。要获得新的值D_{j,k}缩小矢量相同的比例，相应的\chi^2_{j,k}一词。我
值----------Value----------
RELEASE----------RELEASE----------
Version 3.9.6 (Although Copyright Tim Downie 1995-6).
版本3.9.6（虽然版权蒂姆·唐尼1995-96）。
注意----------Note----------
POLICIES
政策
singleIf policy="single" then univariate thresholding is applied to each element of D as in (Strela et al 1999).
singleIfpolicy="single"然后单变量的阈值被施加的每个元素的D如（箭等1999）。
universalThe universal threshold is computed using 2log(n) (See Downie & Silverman 1996) where n is the number of coefficient vectors to be thresholded.
universalTheuniversal阈值使用2log（n）的（见1996年唐尼和Silverman）其中，n是数以将阈值的系数矢量计算。
manualThe "manual" policy is simple. You supply a threshold value to the threshold argument and hard or soft thresholding is performed using that value
manualThe“manual”政策很简单。您提供了一个threshold值的阈值参数和硬或软阈值，使用该值进行
（作者）----------Author(s)----------
Tim Downie
参见----------See Also----------
accessC.mwd, accessD.mwd, draw.mwd, mfirst.last, mfilter.select, mwd, mwd.object, mwr, plot.mwd, print.mwd, putC.mwd, putD.mwd, summary.mwd, wd, wr.mwd.
accessC.mwd，accessD.mwd，draw.mwd，mfirst.last，mfilter.select，mwd，mwd.object，mwr，plot.mwd，print.mwd，putC.mwd，putD.mwd，summary.mwd，wd，wr.mwd。
实例----------Examples----------
#[]
# Generate some test data[生成一些测试数据。]
#[]
test.data <- example.1()$y
## Not run: ts.plot(test.data)[＃不运行：ts.plot（test.data）]
#[]
# Generate some noisy data[产生一些噪声数据]
#[]
ynoise <- test.data + rnorm(512, sd=0.1)
##[＃]
# Plot it[画出它]
#[]
## Not run: ts.plot(ynoise)[＃不运行：ts.plot（ynoise）]
#[]
# Now take the discrete multiple wavelet transform[现在采取的离散多小波变换]
# N.b. I have no idea if the default wavelets here are appropriate for[注意：我不知道，如果默认情况下的小波这里是适合]
# this particular examples. [这个特殊的例子。]
#[]
ynmwd <- mwd(ynoise)
## Not run: plot(ynwd)[＃不运行：图（ynwd）]
# [1] 2.020681 2.020681 2.020681 2.020681 2.020681 2.020681 2.020681[[1] 2.020681 2.020681 2.020681 2.020681 2.020681 2.020681 2.020681]
#[]
# Now do thresholding. We'll use the default arguments.[现在做的阈值。我们将使用默认的参数。]
#[]
ynmwdT <- threshold(ynmwd)
#[]
# And let's plot it[让我们把它标绘]
#[]
## Not run: plot(ynmwdT)[＃不运行：图（ynmwdT）]
#[]
# Let us now see what the actual estimate looks like[现在让我们看看有什么实际的估计看起来像]
#[]
ymwr <- wr(ynmwdT)
#[]
# Here's the estimate... [这里的估计...]
#[]
## Not run: ts.plot(ywr1)[＃不运行：ts.plot（ywr1）]
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 20:12:50
