WaveD(waved)
WaveD()所属R语言包：waved
WaveD
挥挥手
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Performs statistical wavelet deconvolution using Meyer wavelet.
执行统计子波反褶积，采用Meyer小波。
用法----------Usage----------
WaveD(yobs, g = c(1, rep(0, (length(yobs) - 1))), MC = FALSE, SOFT = FALSE, F = find.j1(g, scale(yobs))[2], L = 3, deg = 3, eta = sqrt(6), thr = maxithresh(yobs, g, eta = eta), label = "WaveD")
参数----------Arguments----------
参数：yobs
Sample of f*g + (Gaussian noise), a vector of dyadic length (i.e. 2^(J-1) where J is the largest resolution level). Here f is the target function, g is the convolution kernel.
样本f*g+（高斯噪声），矢量并矢长度（即2^(J-1)J是最大分辨率级别）。在这里，f为目标函数，g为卷积核。
参数：g
Sample of g or g + (Gaussian noise), same length as yobs. The default is the Dirac mass at 0.
样品g或g+（高斯噪声），相同长度的小混混。默认值是狄拉克质量为0。
参数：MC
Option to only return the (fast) translation-invariant WaveD estimate (MC=TRUE) as opposed to the full WaveD output (MC=FALSE, the default), as described below. MC=TRUE recommended for Monte Carlo simulation.
选项，只返回（快）翻译不变挥手估计，（MC = TRUE），而不是全挥手输出（MC = FALSE，默认值），如下所述。 MC = TRUE建议的Monte Carlo模拟。
参数：SOFT
if SOFT=TRUE, uses the soft thresholding policy as opposed to the hard (SOFT=FALSE, the default).
如果SOFT = TRUE，采用软阈值策略，而不是硬（软= FALSE，默认值）。
参数：F
Finest resolution level; the default is the data-driven choice j1 (see Value below).
最好的分辨率级别，默认为数据驱动的选择J1（见以下值）。
参数：L
Lowest resolution level; the default is 3.
最低分辨率级别，默认为3次。
参数：deg
The degree of the Meyer wavelet, either 1, 2, or 3 (the default).
的程度，Meyer小波，1，2，或3（默认值）。
参数：eta
Tuning parameter of the maxiset threshold; default is √(6).
调整参数的maxiset阈值;默认是√(6)。
参数：thr
A vector of length F-L+1, giving thresholds at each resolution levels L,L+1,...,F; default is maxiset threshold.
，在每个分辨率级别的阈值的矢量长度F-L+1L,L+1,...,F;默认是maxiset的阈值。
参数：label
Auxiliary plotting parameter; do not change this.
辅助绘图参数，不改变这一点。
值----------Value----------
In the case that MC=TRUE, WaveD returns a vector consisting of the translation-invariant WaveD estimate. In the case that MC=FALSE (the default), WaveD returns a list with components <table summary="R valueblock"> <tr valign="top"><td>waved</td> <td> translation invariant WaveD transform; in the case MC=TRUE this is all that is returned.</td></tr> <tr valign="top"><td>ordinary</td> <td> ordinary WaveD transform</td></tr> <tr valign="top"><td>FWaveD</td> <td> Forward WaveD Transform; see FWaveD.</td></tr> <tr valign="top"><td>w</td> <td> alternate name for FWaveD</td></tr> <tr valign="top"><td>w.thr</td> <td> thresholded version of w</td></tr> <tr valign="top"><td>IWaveD</td> <td> Inverse WaveD Transform</td></tr> <tr valign="top"><td>iw</td> <td> alternate name for IWaveD</td></tr> <tr valign="top"><td>s</td> <td> estimate of the noise standard deviation</td></tr> <tr valign="top"><td>j1</td> <td> estimate of optimal resolution level (for maxiset threshold).</td></tr> <tr valign="top"><td>F</td> <td> Fine resolution level used (may be different to j1).</td></tr> <tr valign="top"><td>M</td> <td> estimate of optimal Fourier frequency (for maxiset threshold).</td></tr> <tr valign="top"><td>thr</td> <td> vector of thresholds used (default is maxiset threshold).</td></tr> <tr valign="top"><td>percent</td> <td> percentage of thresholding per resolution level</td></tr> <tr valign="top"><td>noise</td> <td> noise proxy, wavelet coefficients of the raw data at the largest resolution level, used for estimating noise features.</td></tr> <tr valign="top"><td>ps</td> <td> P-value of the Shapiro-Wilk test for normality applied to the noise proxy.</td></tr> <tr valign="top"><td>residuals</td> <td> wavelet coefficients that have been removed before fine level F.</td></tr> </table>
在MC = TRUE的情况下，挥手返回向量的平移不变性挥手估计。在的情况下，MC = FALSE（默认值），挥挥手，返回一个列表的组件<table summary="R valueblock"> <tr valign="top"> <TD> waved</ TD> <TD>平移不变挥手变换的情况下MC = TRUE，则返回。</ TD> </ TR> <tr valign="top"> <TD> ordinary </ TD> <TD>普通挥手变换</ TD> </ TR> <tr valign="top"> <TD>FWaveD</ TD> <TD>正向挥手变换;看到FWaveD。</ TD> </ TR> <tr valign="top"> <TD>w </ TD> <TD>备用名称FWaveD </ TD> </ TR> <tr valign="top"> <TD> X> </ TD> <TD>阈值版本的W </ TD> </ TR> <tr valign="top"> <TD>w.thr </ TD> <TD>反挥手变换</ TD> </ TR> <tr valign="top"> <TD>IWaveD </ TD> <TD>备用名称IWaveD </ TD> </ TR> <tr valign="top"> < iw TD> </ TD> <TD>估计的噪声标准差</ TD> </ TR> <tr valign="top"> <TD>s </ TD> <TD估计最佳分辨率（为maxiset阈值）。</ TD> </ TR> <tr valign="top"> <TD>j1 </ TD> <TD>精细分辨率水平（可能是不同的J1）。</ TD> </ TR> <tr valign="top"> <TD>F </ TD> <TD>估计的最佳傅立叶频率（为maxiset的阈值）。</ TD > </ TR> <tr valign="top"> <TD>M </ TD> <TD>矢量的阈值（默认是maxiset的阈值的）。</ TD> </ TR> <TR VALIGN =“”> <TD> thr </ TD> <TD>每分辨率级别的阈值百分比</ TD> </ TR> <tr valign="top"> <TD> percent <TD>噪声代理，小波系数最大分辨率级别的原始数据，用于估计噪声功能</ TD> </ TD> </ TR> <tr valign="top"> <TD><X > </ TD> <TD> P-Shapiro-Wilk检验正常的噪声代理。</ TD> </ TR> <tr valign="top"> <TD>noise </ TD> <TD>已被删除之前精细程度的小波系数，F. </ TD> </ TR> </ TABLE>
（作者）----------Author(s)----------
Marc Raimondo and Michael Stewart
参考文献----------References----------
Cavalier, L. and Raimondo, M. (2007), ‘Wavelet deconvolution with noisy eigen-values’, IEEE Trans. Signal Process, Vol. 55(6), In the press.
Donoho, D. and Raimondo, M. (2004), ‘Translation invariant deconvolution in a periodic setting’, The International Journal of Wavelets, Multiresolution and Information Processing 14(1),415–423.
Johnstone, I., Kerkyacharian, G., Picard, D. and Raimondo, M. (2004), 'Wavelet deconvolution in a periodic setting', Journal of the Royal Statistical Society, Series B 66(3),547–573. with discussion pp.627–652.
Raimondo, M. and Stewart, M. (2007),
参见----------See Also----------
FWaveD
FWaveD
实例----------Examples----------
library(waved)
data=waved.example(TRUE,FALSE)
doppler.wvd=WaveD(data$doppler.noisy,data$g)
summary(doppler.wvd)
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 16:54:26
