BAYES.THR(wavethresh)
BAYES.THR()所属R语言包：wavethresh
Bayesian wavelet thresholding.
贝叶斯小波阈值。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function carries out Bayesian wavelet thresholding of noisy data, using the BayesThresh method of Abramovich, Sapatinas, & Silverman (1998).
此功能进行贝叶斯小波阈值的噪声数据，使用的BayesThresh的方法阿布拉莫维奇，Sapatinas，和Silverman（1998）。
用法----------Usage----------
BAYES.THR(data, alpha = 0.5, beta = 1, filter.number = 8, family =
"DaubLeAsymm", bc = "periodic", dev = var, j0 = 5, plotfn = FALSE)
参数----------Arguments----------
参数：data
A vector of length a power of two, containing noisy data to be thresholded.
一个向量两个长度的权力，含有噪声的数据将阈值。
参数：alpha, beta
Hyperparameters which determine the priors placed on the wavelet coefficients. Both alpha and beta take positive values; see Abramovich, Sapatinas, & Silverman (1998) or Chipman & Wolfson (1999) for more details on selecting alpha and beta.
蔡怀平确定对上的小波系数下的先验概率。 α和β都取正值阿布拉莫维奇，Sapatinas，和西尔弗曼（1998）或奇普曼欧胜（1999）有关详细信息，选择alpha和beta。
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。
For the “wavelets on the interval” (bc="interval") transform the filter number ranges from 1 to 8. See the table of filter coefficients indexed after the reference to Cohen, Daubechies and Vial, (1993).
对于“小波的时间间隔”（bc="interval"）变换过滤器的数量范围从1到8。见科恩，Daubechies小波和样品瓶，（1993）后，参照表中的滤波器系数的索引。
参数：family
Specifies the family of wavelets that you want to use. Two popular options are "DaubExPhase" and "DaubLeAsymm" but see the help for filter.select for more possibilities.
指定要使用的小波家庭。两种流行的的选项“DaubExPhase”和“DaubLeAsymm”，但看到更多的可能性filter.select的帮助。
This argument is ignored for the “wavelets on the interval” transform (bc="interval").
此参数将被忽略“小波”的时间间隔变换（bc="interval"）。
参数：bc
Specifies the boundary handling. If bc="periodic" the default, then the function you decompose is assumed to be periodic on it's interval of definition, if bc="symmetric" then the function beyond its boundaries is assumed to be a symmetric reflection of the function in the boundary. The symmetric option was the implicit default in releases prior to 2.2. Ifbc=="interval" then the “wavelets on the interval algorithm” due to Cohen, Daubechies and Vial is used. (The WaveThresh implementation of the “wavelets on the interval transform” was coded by Piotr Fryzlewicz, Department of Mathematics, Wroclaw University of Technology, Poland; this code was largely based on code written by Markus Monnerjahn, RHRK, Universitat Kaiserslautern; integration into WaveThresh by GPN).
指定的边界处理。如果bc="periodic"默认值，然后你分解的功能被认为是周期性的，它的定义的时间间隔，如果bc="symmetric"那么的功能超越它的界限，在边界假设是对称反射的功能， 。对称隐含的默认选项是2.2之前的版本中。如果bc=="interval"然后在“区间上的小波算法”由于科恩，Daubechies小波和样品瓶的使用。 （WaveThresh实施的“区间上的小波变换”编码由彼得·Fryzlewicz的技术，波兰的弗罗茨瓦夫大学，数学系，这主要基于代码编写的代码是由Markus Monnerjahn，RHRK的Universitat凯泽斯劳滕融入WaveThreshGPN）。
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：j0
The primary resolution level. While BayesThresh thresholds at all resolution levels, j0 is used in assessing the universal threshold which is used in the empirical Bayes estimation of hyperparameters.
主要的分辨率级别。虽然分辨率级别的BayesThresh阈值，j0是在评估中所用的经验Bayes估计的超全局阈值。
参数：plotfn
If TRUE, BAYES.THR draws the noisy data and the thresholded function estimate.
如果是TRUE，BAYES.THR绘制噪声数据，并且阈值函数估计。
Details
详细信息----------Details----------
A mixture prior consisting of a zero-mean normal distribution and a point mass at zero is placed on each wavelet coefficient. The empirical coefficients are then calculated and the priors updated to give posterior distributions for each coefficient. The thresholded value of each coefficient is the median of that coefficient's posterior distribution. See Abramovich, Sapatinas, & Silverman (1998) for more details of the procedure; the help page for threshold.wd has more information about wavelet thresholding in general.
前的混合物组成的一个零均值的正常分布和质点在零被放置在每个小波系数。经验系数，然后计算和更新的先验概率为每个系数，得到的后验分布。每个系数的阈值值是该系数的后验分布的中位数。阿布拉莫维奇，Sapatinas，和Silverman（1998年）的程序的详细信息的帮助页面threshold.wd一般的小波阈值的更多信息。
The function wave.band uses the same priors to compute posterior credible intervals for the regression function, using the method described by Barber, Nason, & Silverman (2001).
的功能wave.band使用相同的先验概率计算后的回归函数的置信区间，理发师，利晨和Silverman（2001）所描述的使用方法。
值----------Value----------
A vector containing the thresholded estimate of the function from which the data was drawn.
一种向量，包含阈值估计的功能，从该数据被绘制。
RELEASE----------RELEASE----------
3.9.5 Code by Fanis Sapatinas/Felix Abramovich Documentation by Stuart Barber
3.9.5守则Fanis的Sapatinas /费利克斯·阿布拉莫维奇文档斯图尔特理发
（作者）----------Author(s)----------
G P Nason
参见----------See Also----------
threshold.wd, wd
threshold.wd，wd
实例----------Examples----------
#[]
# Generate some noisy test data and plot it.[产生一些噪声测试数据和绘制。]
#[]
blocks.data <- DJ.EX(n=512, noisy=TRUE)$blocks
#[]
# Now try BAYES.THR with the default parameters.[将用默认参数现在尝试BAYES.THR的。]
#[]
blocks.thr <- BAYES.THR(blocks.data, plotfn=TRUE)
#[]
# The default wavelet is Daubechies' least asymmetric wavelet [默认的小波是Daubechies小“不对称小波]
# with 8 vanishing moments; quite a smooth wavelet. Since the [8消失矩;相当平稳小波。由于]
# flat sections are still rather noisy, try Haar wavelets:[平坦的路段仍然相当嘈杂，尝试Haar小波：]
# []
blocks.thr <- BAYES.THR(blocks.data, plotfn=TRUE, filter.number=1,
family = "DaubExPhase")
#[]
# To show the importance of a sensible prior, consider alpha = 4, [要显示前一个明智的重要性，认为α= 4，]
# beta = 1 (which implies a smoother prior than the default). [β= 1（这意味着一个平滑的前比默认的）。]
#[]
blocks.thr <- BAYES.THR(blocks.data, plotfn=TRUE, filter.number=1,
family = "DaubExPhase", alpha=4, beta=1)
#[]
# Here, the extreme values of the function are being smoothed towards zero.[在这里，该函数的极值正在向零平滑。]
#[]
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 17:13:37
