wavCWTTree(wmtsa)
wavCWTTree()所属R语言包：wmtsa
Tree map of continuous wavelet transform extrema
连续小波变换极值树图
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function first finds the extrema locations (in time and in scale) of the continuous wavelet transform input. The set of extrema are then subdivided into sets of branches, where each branch represents a collection of extrema that correspond to the same ridge in the CWT time-scale plane. A coarse-to-fine scale strategy is used to identify the members of each branch as follows: (i) a single extremum at the coarsest scale is selected as the start of a given branch, (ii) the closest neighboring extremum in time at the next finest scale is then added to the branch, (iii) step ii is repeated until the smallest scale is reached or an apparent break occurs in the branch across scale, and (iv) steps i-iii are repeated until all extrema have been accounted. A branch is not grown unless the nearest neighbor candidate at the next finest scale is close in time to the last recorded branch member, where "close" is defined as being less than the current scale of the neighbor candidate. This means that the window in time for admissible neighbor extrema candidates (at the next finest scale) shrinks
这个函数首先发现的连续小波变换输入的极值的位置（在时间和规模）。然后再分成组的分支，每个分支表示的集合的极值，对应于相同的脊在CWT的时间尺度平面组极值。甲粗到细尺度策略被用来识别每个分支的成员如下：（ⅰ）一个单一的极值在粗尺度被选作一个给定的分支的开始，（ii）该时间最接近的相邻极值然后，下一个最好的比例添加到分支，（ⅲ）步骤ii被重复，直到达到最小刻度或表观断裂发生在分支横跨规模，及（iv）步骤i-ⅲ被重复，直到所有的极值已经占。一个分支还没有生长，除非近邻候选人，下一个最好的规模是在时间上接近的最后记录的分支成员，其中，“接近”被定义为小于当前规模邻居候选。这意味着，在受理相邻极值候选人的时间窗口（在下一个最好的规模）收缩
用法----------Usage----------
wavCWTTree(x, n.octave.min=1, tolerance=0.0, type="maxima")
参数----------Arguments----------
参数：x
an object of class wavCWT (as produced by the wavCWT function).
类的一个对象wavCWT（wavCWT功能）。
参数：n.octave.min
a pruning factor for excluding non-persistent branches. If a branch of connected extrema does not span this number of octaves, it is excluded from the tree. Default: 1.
修剪因素，不包括非持续性分支机构。连接极值的一个分支，如果不跨越这个数字的八度音，将被排除从树上。默认值：1。
参数：tolerance
a tolerance vector used to find CWT extrema. This vector must be as long as there are scales in the CWT such that the jth element defines the tolerance to use in finding modulus maxima at the jth scale of the CWT. If not, the last value is replicated appropriately. Default: 0.
一个宽容矢量用于到CWT极值。该向量必须是只要有尺度在CWT使得jth元素定义的公差使用在寻找模极大在jth规模的CWT。如果不是这样，最后的值适当地被复制。默认值：0。
参数：type
a character string denoting the type of extrema to seek in the CWT plane. Supported types are "extrema", "maxima" and "minima". Default: "maxima".
表示一个字符串类型的极值，寻求在CWT平面。支持的类型"extrema"，"maxima"和"minima"。默认值："maxima"。
Details
详细信息----------Details----------
A point in the CWT W(t,j) is defined as an extremum if |W(t-1,j)| + tol < |W(t,j)| and |W(t+1,j)| + tol < |W(t,j)| where tol is a (scale-dependent) tolerance specified by the user. The search algorithm is also adpated to identify plateaus in the data, and will select the the middle of the plateau as a maximum location when encountered. The data |W(t,j)| is first scaled so that its maximum value is 1.0, so the tolerances should be adjusted accordingly. Since the CWT coefficients are (in effect) a result band-pass filtering operations, the large scale coefficients form a smoother curve than do the small scale coefficients. Thus, the tolerance vector allows the user to specify scale-dependent tolerances, helping to weed out undesirable local maxima. It is recommended that the tolerance be set proportional to the scale, e.g., tolerance=C / sqrt(scale) where C is a constant 0 < C < 1. The user is also allowed to control the types of peaks to pursue in the CWT plane: extrema, maxima, or minima. The algorithm (described above) is adjusted accordingly.
被定义为一个极值，如果在A点的CWT W(t,j) |W(t-1,j)| + tol < |W(t,j)|和|W(t+1,j)| + tol < |W(t,j)|其中tol是一个由用户指定的公差（规模而定）。搜索算法也adpated识别数据中的高原，并且将选择的高原中部时，遇到的最大位置。的数据|W(t,j)|首先被缩放，使得它的最大值为1.0，所以公差应作相应调整。由于CWT的系数是（实际上）结果带通滤波操作，大规模的系数形成一个平滑的曲线，比小规模系数。因此，容差向量允许用户指定比例相关的公差，帮助淘汰不希望的局部极大值。它的耐受性成正比的规模，例如tolerance=C / sqrt(scale)其中C是一个常数0 < C < 1的建议。用户还可以控制在CWT平面极值，最大值，最小值峰的类型，以追求。的算法（如上所述）进行相应的调整。
The output object contains a list of sublists, each sublist corresponds to a single branch in the CWT tree and contains the named vectors:
输出对象包含了一系列的子表，每个子表对应的CWT树的一个分支和包含命名向量：
itimeindex locations in time of CWT extrema
itimeindex地点，时间CWT极值
iscaleindex locations in scale of CWT extrema
iscaleindex地点，规模CWT极值
timetimes associated with CWT extrema
timetimes与CWT极值
scalescales associated with CWT extrema
scalescales与CWT极值
extremaCWT extrema values
extremaCWT极端值
In addition, the returned object contains the following attributes:
此外，返回的对象包含以下属性：
iendtimeinteger vector of indices corresponding to the locations in time where the branches terminated as the scale appraoches zero.
iendtimeinteger矢量终止分支作为规模appraoches为零的时间中的位置相对应的索引。
endtimenumeric vector containing branch termination times
endtimenumeric向量的分公司终止时间
timenumeric vector of times corresponding to the original time series
timenumeric对应的原来的时间序列矢量的次数
scalenumeric vector of scales used to form the CWT
scalenumeric向量的尺度用于形成CWT
extrema.maska binary matrix (of the same dimension as the CWT matrix) containing a 1 where there exists a corresponding extremum value in the CWT plane
extrema.maska二进制矩阵（作为CWT矩阵具有相同维数），含有1，其中存在一个相应的极值在CWT的平面
noisea numeric vector containing the first scale's CWT coefficients. Statistical analysis of these data are often used as a rough estimate of the (local) noise level(s) in the original time series.
noisea数字向量第一规模的CWT系数。这些数据的统计分析，经常被用来作为一个粗略的估计的（本地）噪声电平（s）在原来的时间序列。
branch.hista numeric vector containing the sum across time of all extrema values. This data can be used to help automate the selection of scales of interest in the CWT plane.
跨越时间的所有极端值branch.hista数字向量的总和。这些数据可以用来帮助自动选择尺度的在CWT平面的兴趣。
值----------Value----------
an object of class wavCWTTree. See DETAILS section for more information.
对象类wavCWTTree。更多信息，请参阅详细信息部分。
S3方法----------S3 METHODS----------
[extracts a subset of branches from the tree. For example, to extract branches 2 through 5, use the syntax x[2:5]. To extract branches which terminate near times 0.47, 0.3, and 1.4, use the syntax x[time=c(0.47, 0.3, 1.4)]. To extract all branches which terminate between times 1.2 and 1.5, use the syntax x[range=c(1.2, 1.5)].
[中提取的一个子集树的分支。例如，要提取分支2到5，使用的语法x[2:5]。要提取的分支，终止近0.47，0.3，和1.4倍，使用的语法x[time=c(0.47, 0.3, 1.4)]。要提取的所有分支，终止1.2和1.5倍，使用的语法x[range=c(1.2, 1.5)]。
plotplots the WTMM tree. The plot method also supports the following optional arguments (assume that the variable x is an output of the wavCWTTree function):
plotplots的WTMM树。图法还支持以下可选参数（假设变量x是一个输出的wavCWTTree函数）：
fit A logical flag. If fit=TRUE, a subset of branches (limited to four) are fit with various linear regression models on a log(|WTMM|) versus log(scale) basis. The models are specified by the optional models argument. This scheme illustrates the process by which exponents are estimated using the WTMM branches. For example, to see the regressions over chains 10 through 13, issue plot(x[10:13], fit=TRUE). Default: FALSE.
适合一个逻辑标志。如果fit=TRUE，分支的一个子集（4）适合各种线性回归模型log（WTMM |）与log（规模）的基础上。该机型的可选的的models参数所指定的。此方案说明指数的过程，估计使用WTMM分支。例如，看到的回归，在连锁10到13，发行plot(x[10:13], fit=TRUE)。默认值：FALSE。
modelsA vector of character strings denoting the linear models to use in illustring the calculation of exponents. This argumetn is used only if fit=TRUE. Default: c("lm", "lmsreg", "ltsreg").
modelsA向量的字符串表示的线性模型使用在illustring指数的计算。此argumetn使用只有fit=TRUE。默认值：c("lm", "lmsreg", "ltsreg")。
labelsLogical flag. If TRUE, the branch number is placed at the head of each branch. Default: TRUE.
labelsLogical标志。如果TRUE，分枝数被放置在每个分支的头。默认值：TRUE。
extremaA logical flag. If TRUE, the locations of the (non-pruned and unbranched) extrema are marked in the time-scale plane. Default: FALSE.
extremaA逻辑标志。如果TRUE，（非修剪和无支链的）的极值的位置标识的时间尺度的平面。默认值：FALSE。
pchThe marker used in plotting branch points via the par function. Default: "o".
pchThe标记策划分支点，通过par功能。默认值："o"。
printprints a summary of the object.
概要对象printprints。
summarysumamrizes the branches comprising the tree ala a data.frame object
summarysumamrizes组成的分支树阿拉数据框对象
参考文献----------References----------
J.F. Muzy, E. Bacry, and A. Arneodo., “The multifractal formalism revisited with wavelets.", International Journal of Bifurcation and Chaos, 4, 245–302 (1994).
参见----------See Also----------
实例----------Examples----------
## create linchirp series [＃创建linchirp系列]
linchirp <- make.signal("linchirp")
## calculate the CWT [＃计算CWT]
W <- wavCWT(linchirp)
## form CWT tree [＃形成CWT树]
W.tree <- wavCWTTree(W)
## print the object [＃打印对象]
print(W.tree)
## summarize the object [＃总结的对象]
summary(W.tree)
## plot thea CWT image with a tree overlay [＃图西娅的CWT图像与树覆盖]
## (R-only) [＃（只R-）]
plot(W)
if (is.R()) plot(W.tree, extrema=TRUE, add=TRUE)
## plot all CWT tree branches [＃图所有CWT的树枝]
plot(W.tree)
## plot a subset of CWT tree branches [＃图CWT树枝的一个子集]
plot(W.tree[5:10])
## plot an illustration of the Holder exponent [＃图的说明，持有人指数]
## estimation process. select branches between [＃估计过程。选择分支之间]
## times 0.2 and 0.4 (only the first four found [＃0.2和0.4倍（仅第4位认为]
## will be fitted) [＃将配备）]
plot(W.tree[range=c(0.2, 0.4)], fit=TRUE)
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 22:53:03
