rsswav(wavethresh)
rsswav()所属R语言包：wavethresh
Compute mean residual sum of squares for odd prediction of even ordinates and vice versa
计算平均残差平方和为奇数的预测，甚至坐标，反之亦然
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Compute mean of residual sum of squares (RSS) for odd prediction of even ordinates and vice versa using wavelet shrinkage with a specified threshold. This is a subsidiary routine of the WaveletCV cross validation function. A version implemented in C exists called Crsswav.
计算平均值为奇数甚至坐标，反之亦然使用一个指定的阈值小波收缩与预测残差平方和（RSS）。这是一间附属公司的日常WaveletCV交叉验证功能。用C语言实现的版本存在名为Crsswav。
用法----------Usage----------
rsswav(noisy, value = 1, filter.number = 10, family = "DaubLeAsymm",
thresh.type = "hard", ll = 3)
参数----------Arguments----------
参数：noisy
A vector of dyadic (power of two) length that contains the noisy data that you wish to compute the averaged RSS for.
矢（幂）的矢量长度包含噪声的数据，你想计算的平均RSS。
参数：value
The specified threshold.
指定的阈值。
参数：filter.number
This selects the smoothness of wavelet that you want to perform wavelet shrinkage by cross-validation.
这将选择要通过交叉验证进行小波收缩小波的平滑度。
参数：family
specifies the family of wavelets that you want to use. The options are "DaubExPhase" and "DaubLeAsymm".
指定要使用的小波家庭。的选项“DaubExPhase”和“DaubLeAsymm”。
参数：thresh.type
this option specifies the thresholding type which can be "hard" or "soft".
此选项指定的阈值类型，可以是“硬”或“软”。
参数：ll
The primary resolution that you wish to assume. No wavelet coefficients that are on coarser scales than ll will be thresholded.
你愿意承担的主要决议。没有将阈值的小波系数是粗糙的尺度比LL。
Details
详细信息----------Details----------
Note: a faster C based implementation of this function called Crsswav is available. It takes the same arguments and returns the same values.
注：此功能称为Crsswav更快的基于C实现的是。它采用相同的参数，并返回相同的值。
Two-fold cross validation can be computed for a wd object using the "cv" policy option in threshold.wd. As part of this procedure for each threshold value that the CV optimisation algorithm selects a RSS value must be computed (the CV optimisation algorithm seeks to minimize this RSS value).
可以计算的WD对象，使用“CV”的政策选择threshold.wd两折交叉验证。作为此过程的一部分，每个阈值的CV优化算法选择一个RSS值必须计算（的CV优化算法旨在尽量减少这种RSS值）。
The RSS value computed is this. First, the even and odd indexed values are separated. The even values are used to construct an estimate of the odd true values using wavelet shrinkage with the given threshold. The sum of squares between the estimate and the noisy odds is computed. An equivalent calculation is performed by swapping the odds and evens. The two RSS values are then averaged and the average returned. This algorithm is described more fully in Nason, (1996).
RSS计算出的值是这样的。首先，索引值的偶数和奇数是分开的。偶数的值被用于构建使用与给定的阈值的小波收缩的奇数的真值的估计。预算和嘈杂的赔率之间的平方的总和计算。一个等价的计算进行交换的可能性，均匀。两个RSS值，然后平均，并且平均返回。此算法利晨，（1996）中更全面地描述。
值----------Value----------
A list with the following components <table summary="R valueblock"> <tr valign="top"><td>ssq</td> <td> The RSS value that was computed</td></tr> <tr valign="top"><td>df</td> <td> The dof value computed on the thresholded wavelet transform of the data with the given threshold and thresholding options. (Although this is not really used for anything).</td></tr> <tr valign="top"><td>value</td> <td> The value argument that was specified.</td></tr> <tr valign="top"><td>type</td> <td> the thresh.type argument that was specified.</td></tr> <tr valign="top"><td>lev</td> <td> The vector ll nlevels(noisy)-1) (i.e. the levels that were thresholded).</td></tr> </table>
以下组件<table summary="R valueblock"> <tr valign="top"> <TD> ssq</ TD> <TD>的RSS值，计算</ TD> </列表TR> <tr valign="top"> <TD> df</ TD> <TD>的自由度计算值阈值小波变换的数据与给定的阈值和阈值选项。 （虽然这不是真正用于任何）。</ TD> </ TR> <tr valign="top"> <TD> value </ TD> <TD>的值指定的参数，。 / TD> </ TR> <tr valign="top"> <TD>type </ TD> <TD>thresh.type参数中指定。</ TD> </ TR> < TR VALIGN =“顶”> <TD>lev </ TD> <TD>的矢量llnlevels(noisy)-1)（即水平的阈值）。</ TD> </ TR> </ TABLE>
（作者）----------Author(s)----------
G P Nason
参见----------See Also----------
Crsswav,threshold.wd, WaveletCV
Crsswav，threshold.wd，WaveletCV
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 18:20:00
