R语言 wavethresh包 mwd.object()函数中文帮助文档(中英文对照)

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mwd.object(wavethresh)
mwd.object()所属R语言包：wavethresh

                                        Multiple wavelet decomposition object (1D)
                                         多小波分解对象（1D）

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

These are objects of class
这些对象的类

mwd
mwd

They represent a decomposition of a function with respect to a multiple wavelet basis.
它们代表了一个分解的功能相对于一个多小波基。


Details

详细信息----------Details----------

To retain your sanity the C and D coefficients should be extracted by the accessC and accessD functions and put using the putC and putD functions, rather than by the $ operator.
为了保持你的理智，C和DaccessC和accessD函数系数应提取的，并使用putC和putD函数，而不是由<X >运营商。


值----------Value----------

The following components must be included in a legitimate "mwd" object.  <table summary="R valueblock"> <tr valign="top"><td>C</td> <td> a matrix containing each level's smoothed data, each column corresponding to one coefficient vector. The wavelet transform works by applying both a smoothing filter and a bandpass filter to the previous level's smoothed data. The top level contains data at the highest resolution level. Each of these levels are stored one after the other in this matrix. The matrix 'fl.dbase$first.last.c' determines exactly which columns in the matrix, store each level.</td></tr> <tr valign="top"><td>D</td> <td> wavelet coefficient matrix. If you were to write down the discrete wavelet transform of a function then columns of D would be the vector coefficients of the wavelet basis function s. Like the C, they are also formed in a pyramidal manner, but stored in a linear matrix. The storage details are to be found in 'fl.dbase$first.last.d'.</td></tr> <tr valign="top"><td>nlevels</td> <td> The number of levels in the pyramidal decomposition that produces the coefficients. The precise number of levels depends on the number of different wavelet functions used and the preprocessing method used, as well as the number of data points used.</td></tr> <tr valign="top"><td>fl.dbase</td> <td> The first last database associated with this decomposition. This is a list consisting of 2 integers, and 2 matrices. The matrices detail how the coefficients are stored in the C and D components of the "mwd.object". See the help on mfirst.last for more information.</td></tr> <tr valign="top"><td>filter</td> <td> a list containing the details of the filter that did the decomposition. See mfilter.select.</td></tr> <tr valign="top"><td>type</td> <td> either "wavelet" indicating that the ordinary multiple wavelet transform was performed or "station" indicating that the non-decimated multiple wavelet transform was done.</td></tr> <tr valign="top"><td>prefilter</td> <td> Type of preprocessing or prefilter used. This will be specigic for the type of multiple wavelet used.</td></tr> <tr valign="top"><td>date</td> <td> The date that the transform was performed or the mwd object was last modified.</td></tr> <tr valign="top"><td>bc</td> <td> how the boundaries were handled</td></tr>  </table>
以下组件必须被包括在一个合法的“MWD对象。 <table summary="R valueblock"> <tr valign="top"> <TD>C</ TD> <td>一个包含每个级别的平滑化数据的矩阵中，每一列对应于一个系数向量。小波变换的工作原理是应用的平滑滤波器和带通滤波器的先前的水平的平滑化数据。的顶层包含数据的最高分辨率水平。每个这些级别被存储后，其他在这个矩阵之一。矩阵的fl.dbase$first.last.c确定到底是哪矩阵的列存储每个级别。</ TD> </ TR> <tr valign="top"> <TD> D </ TD> < TD>小波系数矩阵。如果你写下的离散小波变换函数的D列是的向量系数小波基函数s。的C一样，它们也形成金字塔形的方式，但存储在一个线性矩阵。存储的细节都可以找到fl.dbase$first.last.d。</ TD> </ TR> <tr valign="top"> <TD> nlevels</ TD> <TD>的数量水平锥体分解产生系数。水平的精确数量取决于使用的不同的小波函数和所使用的预处理方法，以及使用。</ TD> </ TR> <tr valign="top"> <TD>的数量的数据点的数目fl.dbase</ TD> <TD>倒数第一的分解与此相关联的数据库。这是一个列表组成的2个整数，和2矩阵。矩阵的mwd.object，C和D分量的系数存储在详细说明如何。请参阅帮助mfirst.last了解更多信息。</ TD> </ TR> <tr valign="top"> <TD>filter </ TD> <td>一个列表，其中包含的细节做了分解的滤波器。见mfilter.select。</ TD> </ TR> <tr valign="top"> <TD>type </ TD> <TD>是"wavelet"表明，普通多小波变换或"station"表示，非锐减多小波变换进行。</ TD> </ TR> <tr valign="top"> <TD>prefilter </ TD> < TD>使用预处理或预过滤器的类型。这将是specigic的。</ TD> </ TR> <tr valign="top"> <TD> date</ td> <td>使用日期的变换进行多小波的类型或MWD对象的最后修改时间。</ TD> </ TR> <tr valign="top"> <TD>bc </ TD> <TD>的界限如何处理</ TD> </ TR> </ TABLE>


GENERATION----------GENERATION----------

This class of objects is returned from the mwd function to represent a multiple wavelet decomposition of a function. Many other functions return an object of class mwd.
这个类的对象，则返回从mwd函数来表示一个多小波分解的功能。许多其他函数返回的的MWD类的对象。


方法----------METHODS----------

The mwd class of objects has methods for the following generic functions: accessC, accessD, draw, plot, print, putC, putD, summary, threshold, wr.mwd.
MWD类的对象有以下通用功能的方法：accessC，accessD，draw，plot，print，putC， putD，summary，threshold，wr.mwd。


RELEASE----------RELEASE----------

Version 3.9.6 (Although Copyright Tim Downie, 1995-6).
版本3.9.6（虽然版权蒂姆·唐尼，1995-96）。


（作者）----------Author(s)----------


Tim Downie



参见----------See Also----------

accessC.mwd, accessD.mwd, draw.mwd, mfirst.last, mfilter.select, mwd.object, mwr, plot.mwd,print.mwd, putC.mwd, putD.mwd, summary.mwd, threshold.mwd, wd, wr.mwd.
accessC.mwd，accessD.mwd，draw.mwd，mfirst.last，mfilter.select，mwd.object，mwr，plot.mwd，print.mwd，putC.mwd，putD.mwd，summary.mwd，threshold.mwd，wd，wr.mwd。

转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。


注：
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