wtthresh(treethresh)
wtthresh()所属R语言包：treethresh
Compute optimal thresholding partition for a sequence of linked arrays
联阵列的序列计算最优阈值分割
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function carries out the joint thresholding algorithm described in section 5 of Evers and Heaton (2009). Though the function, in principle, can work any sequence of arrays, it is designed to work with blocks of wavelet coefficients. These can be extracted from an wd or imwd object using the function extract.coefficients.
此功能进行Evers和希顿（2009年）第5条中描述的联合阈值算法。虽然该功能，在原则上，可以工作的任何序列的阵列，它被设计成与小波系数的块。这些可以提炼出一个wd或imwd反对使用的功能extract.coefficients。
用法----------Usage----------
wtthresh(data, beta, weights, control=list())
参数----------Arguments----------
参数：data
A list containing the arrays to be thresholded. The elements of the array have to be arrays of the same number of dimensions. The arrays can be of different sizes, however the ratio of their side lengths has to be the same. Can be a list of wavelet coefficients extracted using extract.coefficients. The data is assumed to have noise of unit variance, thus the data needs to be rescaled a priori (e.g. in the case of wavelet coefficients using function estimate.sdev).
一个列表包含的阵列阈值。该数组的元素具有相同的维数是数组。该阵列可以具有不同的尺寸，但是他们的边的长度的比率是相同的。可以是列表的小波系数提取使用extract.coefficients。数据被认为有噪声的单位方差，因此，需要对数据进行重新调整先验的（例如，在小波系数的情况下，使用功能estimate.sdev）。
参数：beta
Instead of using the original data, one can call wtthresh using the beta_i instead of the observed data. These can be computed using beta.laplace.
而不是使用原始的data，可以调用“wtthresh使用beta_i的观测数据。这些可以计算使用beta.laplace。
参数：weights
The different elements of the list can be weighted. This allows for giving greater weight to certain arrays. By default, no weights are used.
列表中的不同元素可以被加权。这允许某些阵列提供了更大的权重。默认情况下，没有权重。
参数：control
A list that allows the user to tweak the behaviour of wtthresh. It can contain the following elements:
一个列表，允许用户调整的行为wtthresh。它可以包含以下内容：
max.depthThe maximum depth of the tree. Defaults to 10.
max.depthThe树的最大深度。默认为10的。
minimum.widthThe minimum width of a region of the partitioning of the largest array. This setting avoids creating too small regions. Defaults to 4.
minimum.widthThe的数组的最大的分区的区域的最小宽度。此设置可避免过小区域。默认为4的。
min.width.scale.factorIf the minimum width of the largest array of size l_0^d is minimum.width, then the minimum width for an array of the size l^2 is minimum.width * (l / l_0) ^ (d * min.width.scale.factor). Defaults to 1.
最大的数组的大小min.width.scale.factorIf的最小宽度l_0^d是minimum.width，然后最小宽度数组的大小l^2的是minimum.width * (l / l_0) ^ (d * min.width.scale.factor)。默认为1的。
min.minimum.widthDo not allow the minimum width of a region to become less than min.minimum.width at any level. Defaults to 1.
min.minimum.widthDo不允许的最小宽度的区域成为小于min.minimum.width在任何级别。默认为1的。
minimum.sizeThe minimum size of a region of the partitioning of the largest array. This setting avoids creating too small regions. Defaults to 8^d, where d is the dimension of the arrays.
minimum.sizeThe的最小大小的分区的区域中的数组的最大。此设置可避免过小区域。默认为8^d，d的数组的维数。
min.size.scale.factorIf the minimum size of the largest array of size l_0^d is minimum.size, then the minimum size for an array of the size l^2 is minimum.size * (l / l_0) ^ (min.size.scale.factor). Defaults to 1.
min.size.scale.factorIf的最小尺寸最大的数组的大小l_0^d是minimum.size，然后的大小l^2是minimum.size * (l / l_0) ^ (min.size.scale.factor)，数组的最小尺寸。默认为1的。
min.minimum.sizeDo not allow the minimum size of a region to become less than min.minimum.size at any level. Defaults to 4^d, where d is the dimension of the arrays.
min.minimum.sizeDo不允许的最小大小的区域成为小于min.minimum.size在任何级别。默认为4^d，d的数组的维数。
rescale.quantileIn order to correct for different depths of the splits (due to minimum size and width requirements) the score statistic s is rescaled: (s-q(df,alpha)) / q(df,alpha), where q(df,alpha) is the alpha quantile of a chi square distribution with df degrees of freedom, and alpha is set to rescale.quantile. Defaults to 0.5.
rescale.quantileIn为了纠正对不同深度的分割（由于最小的尺寸和宽度的要求）得分统计s重新调整：(s-q(df,alpha)) / q(df,alpha)q(df,alpha)是alpha 位数的一个chi square df自由度用和alpha设置为rescale.quantile。默认为0.5的。
lr.signifIf the p-value of the corresponding likelihood ratio test is larger than 1-lr.signif a split will be discarded. Defaults to 0.5.
lr.signifIf相应的似然比检验的p-值是大于1-lr.signif拆分将被丢弃。默认为0.5的。
absolute.improvementThe minimum absolute improvement of the above criterion necessary such that a split is retained. Defaults to -Inf, i.e. deactivated.
absolute.improvementThe最小绝对改善的必要的上述标准，使得保留分裂。默认为-Inf，即停用。
relative.improvementThe minimum relative improvement of the above criterion necessary such that a split is retained. Defaults to -Inf, i.e. deactivated.
relative.improvementThe最小相对改善的必要的上述标准，使得分裂被保留。默认为-Inf，即停用。
aThe parameter a of the Laplace distribution gamma(mu) = const * exp(-a*mu) corresponding to the signal. Defaults to 0.5.
七成参数a的拉普拉斯分布gamma(mu) = const * exp(-a*mu)对应于该信号。默认为0.5的。
beta.maxThe maximum value of beta. Defaults to 1e5.
beta.maxThe最高值beta。默认为1e5的。
max.iterThe maximum number of iterations when computing the estimate of the weight w in a certain region. Defaults to 30.
max.iterThe最大迭代次数，当计算的估计的重量w在一定区域内。默认为30的。
tolerance.gradThe estimate of the weight w in a certain region is considered having converged, if the gradient of the likelihood is less than tolerance.grad. Defaults to 1e-8.
tolerance.gradThe估计的重量w在一定的区域被认为是具有融合，如果梯度的可能性小于tolerance.grad。默认为1e-8的。
toleranceThe estimate of the weight w in a certain region is considered having converged, if the estimates of the weight w change less than tolerance. Defaults to 1e-6.
toleranceThe估计的重量w在一定区域内的被认为是融合的，如果估计的重量w改变小于tolerance。默认为1e-6的。
值----------Value----------
wtthresh returns an object of the class c("wtthresh"), which is a list containing the following elements: <table summary="R valueblock"> <tr valign="top"><td>splits</td> <td> A table describing the structure of the fitted tree together with the local loglikelihoods required for the pruning.</td></tr> <tr valign="top"><td>details</td> <td> A table giving the details about where the split was carried out for each of the arrays (i.e. for each block of coefficients).</td></tr> <tr valign="top"><td>w</td> <td> The weights w of the mixture component corresponding to the signal for each region as described by the corresponding row of splits.</td></tr> <tr valign="top"><td>t</td> <td> The corresponding hard threshold t for each region as described by the corresponding row of splits.</td></tr> <tr valign="top"><td>membership</td> <td> A list of the same length as data indicating to which region each entry of the arrays of data belongs.</td></tr> <tr valign="top"><td>beta</td> <td> The values of beta for each coefficient (as a list).</td></tr> <tr valign="top"><td>data</td> <td> The data used (as a list).</td></tr> <tr valign="top"><td>weights</td> <td> The weights used for each array of observations/coefficients.</td></tr> <tr valign="top"><td>control</td> <td> The control list of tuning options used. (see argument control).</td></tr> </table>
wtthresh返回一个对象类c("wtthresh")，这是一个列表，其中包含以下元素：<table summary="R valueblock"> <tr valign="top"> <TD>splits </ TD> <td>一个表一起的修剪与需要当地loglikelihoods，，描述结构的拟合树。</ TD> </ TR> <tr valign="top"> <TD>details </ TD> <td>一个表中提供的详细信息进行分割，每个阵列（即每块系数）。</ TD> </ TR> <tr valign="top"> <TD>w</运输署> <td>使用权重w的混合物组分，针对每个区域的信号对应于所描述的相应的行splits。</运输署> </ TR> <tr valign="top"> <TD> t</ TD> <TD>相应的硬阈值t为每个区域所描述的相应行splits 。</ TD> </ TR> <tr valign="top"> <TD>membership </ TD> <td>一个相同长度的列表作为data表明哪个区域各进入阵列的数据属于。</ TD> </ TR> <tr valign="top"> <TD> beta</ TD> <TD>的值beta每个系数（列表）。</ TD> </ TR> <tr valign="top"> <TD> data </ TD> <TD>使用的数据（如列表）。</ TD> </ TR> <tr valign="top"> <TD> weights </ TD> <TD>用于各自为阵的意见/系数的权重。</ TD> </ TR> <TR VALIGN = “顶”> <TD> control </ TD> <TD>控制列表使用的调节选项。 （见参数control）</ TD> </ TR> </表>
注意----------Note----------
For an example of the use of wtthresh, see coefficients.
对于使用wtthresh的一个例子，请参阅coefficients。
参考文献----------References----------
Evers, L. and Heaton T. (2009) Locally Adaptive Tree-Based Thresholding. Journal of Computational and Graphical Statistics. Dec 2009, Vol. 18, No. 4: 961-977.
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 11:54:10
