irregwd(wavethresh)
irregwd()所属R语言包：wavethresh
Irregular wavelet transform (decomposition).
不规则，小波变换（分解）。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function performs the irregular wavelet transform as described in the paper by Kovac and Silverman.
这个函数执行的不规则小波变换科瓦奇和Silverman的文章中所描述的。
用法----------Usage----------
irregwd(gd, filter.number=2, family="DaubExPhase", bc="periodic", verbose=FALSE)
参数----------Arguments----------
参数：gd
A grid structure which is the output of the makegrid function.
一个网格结构，的输出makegrid功能。
参数：filter.number
This selects the smoothness of wavelet that you want to use in the decomposition. By default this is 2, the Daubechies extremal phase orthonormal compactly supported wavelet with 2 vanishing moments.
选择要使用的分解小波的平滑度。默认情况下这是2，2消失矩的Daubechies极值相位正交的紧支撑小波。
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。两个流行的选择是“DaubExPhase”和“DaubLeAsymm的”，但的帮助filter.select更多的可能性。
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.
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。
参数：verbose
Controls the printing of "informative" messages whilst the computations progress. Such messages are generally annoying so it is turned off by default.
控制打印的“信息”的消息，而计算的进展。这样的消息一般都是讨厌的，所以它在默认情况下是关闭的。
Details
详细信息----------Details----------
If one has irregularly spaced one-dimensional regression data (t,y), say. Then the function makegrid interpolates this to a regular grid and then the standard wavelet transform is used to transform the interpolated data. However, unlike the standard wavelet denoising set-up the interpolated data, y, values are correlated. Hence the wavelet coefficients of the interpolated will be correlated (even after using an orthogonal transform). Hence, in particular, the variance of each wavelet coefficient may well be different and so this routine also computes those variances using a fast algorithm (related to the two-dimensional wavelet transform).
如果一个不规则间隔一维的回归数据（T，Y）说。然后，该功能makegrid内插这规则的网格标准的小波变换，然后用于转化的内插数据。然而，不像标准小波去噪设置向上的内插数据y，值是相关的。因此，小波系数的内插将相关（即使使用后的正交变换）。因此，特别是，每一个小波系数的方差可能是不同的，所以该例程也使用快速算法（相关的二维小波变换），计算这些差异。
When thresholding with threshold.irregwd the threshold function makes use of the information about the variance of each coefficient to modify the variance locally on a coefficient by coefficient basis.
当阈值带threshold.irregwd的的阈值函数，使每个系数的方差在本地修改方差系数由系数基础上的信息有关的使用。
值----------Value----------
An object of class irregwd which is a list with the following components.
类的一个对象irregwd是与以下组件的列表。
<table summary="R valueblock"> <tr valign="top"><td>C</td> <td> Vector of sets of successively smoothed versions of the interpolated data (see description of equivalent component of wd.object for further information.)</td></tr> <tr valign="top"><td>D</td> <td> Vector of sets of wavelet coefficients of the interpolated data at different resolution levels. (see description of equivalent component of wd.object for further information.)</td></tr> <tr valign="top"><td>c</td> <td> Vector that aids in calculation of variances of wavelet coefficients (used by threshold.irregwd).</td></tr> <tr valign="top"><td>nlevels</td> <td> The number of resolution levels. This depends on the length of the data vector. If length(data)=2^m, then there will be m resolution levels. This means there will be m levels of wavelet coefficients (indexed 0,1,2,...,(m-1)), and m+1 levels of smoothed data (indexed 0,1,2,...,m).</td></tr> <tr valign="top"><td>fl.dbase</td> <td> There is more information stored in the C and D than is described above. In the decomposition “extra” coefficients are generated that help take care of the boundary effects, this database lists where these start and finish, so the "true" data can be extracted.</td></tr> <tr valign="top"><td>filter</td> <td> A list containing information about the filter type: Contains the string "wavelet" or "station" depending on which type of transform was performed.</td></tr> <tr valign="top"><td>bc</td> <td> How the boundaries were handled.</td></tr> <tr valign="top"><td>date</td> <td> The date the transform was performed.</td></tr> </table>
<table summary="R valueblock"> <tr valign="top"> <TD> C</ TD> <TD>向量的连续平滑的版本插入的数据集（详见同等分量的wd.object进一步的信息。）</ TD> </ TR> <tr valign="top"> <TD>D </ TD> <TD>套小波系数向量的插补数据在不同的分辨率水平。 （详见同等分量的wd.object了解更多信息。）</ TD> </ TR> <tr valign="top"> <TD> c </ TD> <TD>矢量，艾滋病在计算小波系数的方差（threshold.irregwd）</ TD> </ TR> <tr valign="top"> <TD> nlevels </ TD> <TD>的使用分辨率级别的数量。这取决于对数据向量的长度。如果length(data)=2^m，然后有米分辨率的水平。这意味着会有米水平的小波系数（索引为0,1,2，...，第（m-1）），和m +1个水平的平滑化数据（索引0,1,2，...，米）</ TD> </ TR> <tr valign="top"> <TD>fl.dbase </ TD> <TD>有更多的信息存储在C和D比上述。在分解的“额外”系数的产生，帮助照顾的边界效应，这个数据库列出了这些起点和终点，这样的“真实”的数据可以被提取出来。</ TD> </ TR> <TR VALIGN = “顶”> <TD> filter </ TD> <td>一个列表，其中包含的过滤器类型的信息：包含字符串“小波”或“站”根据哪种类型的变换进行。</ TD > </ TR> <tr valign="top"> <TD>bc </ TD> <TD>的界限如何处理。</ TD> </ TR> <tr valign="top"> <TD>date </ TD> <TD>的日期转换。</ TD> </ TR> </ TABLE>
RELESASE----------RELESASE----------
3.9.4 Code Copyright Arne Kovac 1997
3.9.4代码1997年阿恩·科瓦奇
（作者）----------Author(s)----------
Arne Kovac
参见----------See Also----------
makegrid, wd, wr.wd, accessC, accessc, accessD, putD, putC, filter.select, plot.irregwd, threshold.irregwd.
makegrid，wd，wr.wd，accessC，accessc，accessD，putD，putC，filter.select，plot.irregwd，threshold.irregwd。
实例----------Examples----------
#[]
# See full examples at the end of the help for makegrid. [请参阅充分例子在月底的帮助makegrid。]
#[]
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。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-10-1 17:23:12
