threshold.irregwd(wavethresh)
threshold.irregwd()所属R语言包：wavethresh
hold irregularly spaced wavelet decomposition object
持有不规则间隔小波分解对象
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function provides various ways to threshold a irregwd class object.
此功能提供了多种阈值一个irregwd类对象。
用法----------Usage----------
threshold(irregwd, levels = 3 nlevels(wd) - 1), type = "hard",
policy = "universal", by.level = FALSE, value = 0, dev = var, boundary = FALSE, verbose = FALSE, return.threshold = FALSE,
force.sure=FALSE, cvtol = 0.01, Q = 0.05, alpha=0.05, ...)
参数----------Arguments----------
参数：irregwd
The irregularly spaced wavelet decomposition object that you wish to threshold.
的不规则小波分解的对象，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 irregwd object supplied. This is usually any integer from 0 to nlevels(irregwd)-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irregwd对象提供一个有效的水平。这通常是从0到nlevels（irregwd）-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".
确定的阈值，这可以是“硬”或“软”的类型。
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", "LSuniversal", "sure", "cv", "fdr", "op1", "op2", "manual", "mannum", "probability". A description of the policies can be obtained by clicking on the above links.
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，不同的政策主要有："universal"，"LSuniversal"，"sure"，"cv"，"fdr"，"op1"，"op2"， "manual"，"mannum"，"probability"。政策的描述可以通过点击上面的链接。
参数：by.level
If FALSE then a global threshold is computed on and applied to all scale levels defined in levels. If TRUE a threshold is computed and applied separately to each scale level.
如果FALSE然后一个全球性的阈值计算，并适用于所有等级的定义的水平。如果TRUE阈值计算，并分别应用到每一个规模水平。
参数：value
This argument conveys the user supplied threshold. If the policy="manual" then value is the actual threshold value.
这个参数传递的用户提供的阈值。如果policy="manual"那么该值是实际的阈值。
参数：dev
this argument supplies the function to be used to compute the spread of the absolute values coefficients. The function supplied must return a value of spread on the variance scale (i.e. not standard deviation) such as the var() function. A popular, useful and robust alternative is the madmad function.
此参数提供的功能被用于计算的绝对值的系数的传播。提供的函数必须返回一个值传播的方差比例（即不标准差），如var()功能。一个流行的，有用的和强大的另一种方法是madmad功能。
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FALSE然后版本的输入阈值，则返回。
参数：force.sure
If TRUE then the SURE threshold is computed on a vector even when that vector is very sparse. If FALSE then the normal SUREshrink procedure is followed whereby the universal threshold is used for sparse vectors of coefficients.
如果TRUESURE的阈值计算，即使该向量的向量是非常稀少。如果FALSE然后正常SUREshrink程序之后是由此普遍的阈值用于稀疏系数向量。
参数：cvtol
Parameter for the cross-validation "cv" policy.
参数的交叉验证"cv"政策。
参数：Q
Parameter for the false discovery rate "fdr" policy.
参数的虚假发现率"fdr"政策。
参数：alpha
Parameter for Ogden and Parzen's first "op1" and "op2" policies.
参数Ogden和Parzen窗的第一个"op1"和"op2"的政策。
参数：...
other arguments
其他参数
Details
详细信息----------Details----------
This function thresholds or shrinks wavelet coefficients stored in a irregwd object and returns the coefficients in a modified irregwd object. The thresholding step is an essential component of denoising.
此函数的阈值或缩小的小波系数存储在一个irregwd对象，并返回的改性irregwd对象中的系数。阈值的步骤是去噪的一个重要组成部分。
The basic idea of thresholding is very simple. In a signal plus noise model the wavelet transform of signal is very sparse, the wavelet transform of noise is not (in particular, if the noise is iid Gaussian then so if the noise contained in the wavelet coefficients). Thus since the signal gets concentrated in the wavelet coefficients and the noise remains "spread" out it is "easy" to separate the signal from noise by keeping large coefficients (which correspond to signal) and delete the small ones (which correspond to noise). However, one has to have some idea of the noise level (computed using the dev option in threshold functions). If the noise level is very large then it is possible, as usual, that no signal "sticks up" above the noise.
阈值的基本思想是非常简单的。在小波变换的信号的信号加噪声模型是很稀疏，小波变换等的噪声是不（特别是，如果噪声是独立同分布的高斯那么，如果包含的噪声在小波系数）。因此，由于得到的信号中的子波系数和浓缩噪声仍然“蔓延”出来，它是“容易”的分离信号从噪声中的保持大系数（对应的信号），和删除的小的（这对应于噪声） 。然而，有一些想法的噪声电平（使用dev选项的阈值函数计算）。如果噪声电平是非常大的，那么它是可能的，像往常一样，没有信号“，坚持”以上的噪音。
For thresholding of an irregularly spaced wavelet decomposition things are a little different. The original data are irregularly spaced (i.e. [x,y] where the x_i are irregularly spaced) and even if one assumes iid error on the original data once this has been interpolated to a grid by the makegrid function the interpolated data values are not independent. The irregwd function computes the wavelet transform of the interpolated data but also computes the variance of each coefficient using a fast transform. This variance information is stored in the c component of irregwd objects and this function, threshold.irregwd, makes use of this variance information when thresholding each coefficient. For more details see Kovac and Silverman, 2000
对于阈值的不规则小波分解的东西有点不同。原始数据的不规则间隔（即[X，Y]，其中x_i不规则间隔），即使假设独立同分布在原始数据上的错误，一旦这个已经被插值到网格makegrid功能插入的数据值是不独立的。 irregwd函数计算的内插数据的小波变换，但也使用快速变换方法，计算每个系数的方差。这方差的c成分irregwd对象和此功能，信息被存储在threshold.irregwd，利用阈值的每个系数时，这个方差信息。有关详细信息，请参见科瓦奇和Silverman，2000年
Some issues to watch for:
注意的一些问题：
levels The default of levels = 3wd$nlevels - 1) for the levels option most certainly does not work globally for all data problems and situations. The level at which thresholding begins (i.e. the given threshold and finer scale wavelets) is called the primary resolution and is unique to a particular problem. In some ways choice of the primary resolution is very similar to choosing the bandwidth in kernel regression albeit on a logarithmic scale. See Hall and Patil, (1995) and Hall and Nason (1997) for more information. For each data problem you need to work out which is the best primary resolution. This can be done by gaining experience at what works best, or using prior knowledge. It is possible to "automatically" choose a "best" primary resolution using cross-validation (but not yet in WaveThresh).
水平默认的levels = 3wd$nlevels - 1)levels选项当然没有在全球范围的所有数据的问题和情况。在哪一级的阈值开始（即在给定的阈值和更细的刻度小波）称为主分辨率和是唯一的一个特别的问题。在某些方面，主决议选择是非常类似的选择的带宽在内核回归尽管在对数刻度。见厅和Patil（1995）和霍尔和利晨（1997）更多信息。对于每一个数据的问题，你需要的工作，这是最好的小学分辨率。这是可以做到什么效果最好，获得经验或使用先验知识。这是可能的“自动”选择“最佳”的主要决议，采用交叉验证（但尚未WaveThresh）。
Secondly the levels argument computes and applies the threshold at the levels specified in the levels argument. It does this for all the levels specified. Sometimes, in wavelet shrinkage, the threshold is computed using only the finest scale coefficients (or more precisely the estimate of the overall noise level). If you want your threshold variance estimate only to use the finest scale coefficients (e.g. with universal thresholding) then you will have to apply the threshold.wd function twice. Once (with levels set equal to nlevels(wd)-1 and with return.threshold=TRUE to return the threshold computed on the finest scale and then apply the threshold function with the manual option supplying the value of the previously computed threshold as the value options.
其次，各级参数计算和适用的阈值水平参数中指定的水平。为此，它规定的水平。有时，在小波阈值，该阈值计算只用最好的比例系数（或更精确的估计的整体噪声水平）。如果您希望您的阈值的方差估计只有使用最好的规模系数（即通用阈值），那么你将不得不申请threshold.wd函数两次。一旦（与水平设置为等于nlevels（WD）-1与return.threshold=TRUE返回规模最好计算的阈值，然后应用与手动选项供给的先前计算的值的阈值函数阈值的价值选择。
by.levelfor a wd object which has come from data with noise that is correlated then you should have a threshold computed for each resolution level. See the paper by Johnstone and Silverman, 1997.
by.levelfor一个wd对象来自相关的噪音，那么你应该有一个阈值，计算出每个分辨率级别的数据。约翰斯通和Silverman，1997年的文件。
值----------Value----------
An object of class irregwd. This object contains the thresholded wavelet coefficients. Note that if the return.threshold option is set to TRUE then the threshold values will be returned rather than the thresholded object.
对象的类irregwd。该对象包含阈值的小波系数。请注意，如果return.threshold选项被设置成TRUE，然后阈值将被返回，而不是阈值的对象。
RELEASE----------RELEASE----------
Version 3.6 Copyright Guy Nason 1997
版本3.6版权所有1997年盖利晨
（作者）----------Author(s)----------
Arne Kovac
参见----------See Also----------
makegrid, irregwd, irregwd object, accessc,
makegrid，irregwd，irregwd对象，accessc，
实例----------Examples----------
#[]
# See main examples of these functions in the help to makegrid[主要的例子，这些功能的帮助makegrid]
#[]
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 20:12:37
