wavVarTest(wmtsa)
wavVarTest()所属R语言包：wmtsa
Homogeneity test for discrete wavelet transform crystals
离散小波变换晶体均匀性测试
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Tests for homogeneity of variance for each scale of a discrete wavelet transform (DWT) decomposition. Based on the assumption that the DWT decorrelates colored noise processes, the interior wavelet coefficients in a given scale (dj) can be regarded as a zero mean Gaussian white noise process. For a homogeneous distribution of dj, there is an expected linear increase in the cumulative energy as a function of time. The so called D-statistic denotes the maximum deviation of the dj from a hypothetical linear cumulative energy trend. This D-statistic is then compared to a table of critical D-statistics that defines the distribution of D for various sample sizes. Comparing the D-statistic of dj to the corresponding critical values provides a means of quantitatively rejecting or accepting the linear cumulative energy hypothesis. This function performs
方差齐性检验，每一个规模的离散小波变换（DWT）的分解。基于假设DWT的解相关有色噪声过程，室内小波系数在一个给定的比例（dj）可以被视为一个零均值高斯白噪声过程。对于一个均匀分布的dj中，有一个在累积能量作为时间的函数的预期的线性增加。所谓的D-统计量表示的最大偏差dj的一个假设的线性累积能量的趋势。这个D-统计量进行比较，它定义了不同的样本大小分布的D表的关键D-统计。所对应的临界值比较，在D-统计dj定量拒绝或接受该线性累积能量假说提供了一种手段。这个函数执行
用法----------Usage----------
significance=c(0.1,0.05,0.01), lookup=TRUE, n.realization=10000,
参数----------Arguments----------
参数：x
an object of class wavTransform as output by the wavDWT function, a corresponding wavBoundary object, or a numeric vector. In the latter case, wavDWT parameters can be passed to specify the type of wavelet to use and the number of decomposition levels to perform.
类的一个对象wavTransformwavDWT功能，相应的wavBoundary对象，或一个数字矢量输出。 wavDWT参数在后者的情况下，可以通过指定的类型使用的小波分解级别的数目来执行。
参数：lookup
a logical flag for accessing precalculated critical D-statistics. The critical D-statistics are calculated for a variety of sample sizes and significances. If lookup is TRUE, this table is accessed. The table is stored as the matrix object D.table.critical and is loaded with the package. Missing table values are calculated using the input arguments: n.realization, n.repetition and tolerance. Default: TRUE.
一个逻辑标志访问预先计算的的关键D-统计。关键D-统计量的计算各种样品的尺寸和意义。如果lookup是TRUE，此表的访问。表存储为矩阵对象D.table.critical和被加载的包。缺少的表值使用的输入参数：n.realization, n.repetition和tolerance。默认值：TRUE。
参数：n.levels
the number of decomposition levels. Valid only for input not of class wavTransform or wavBoundary. Default: the maximum decomposition level that contains at least one interior wavelet coefficient.
的分解级别的数目。仅适用于输入类wavTransform或wavBoundary。默认值：包含至少一个内部的小波系数的最大分解级别。
参数：n.realization
an integer specifying the number of realizations to generate in a Monte Carlo simulation for calculating the D-statistic(s). This parameter is used either when lookup is FALSE, or when lookup is TRUE and the table is missing values corresponding to the specified significances. Default: 10000.
一个整数，指定数的实现，产生的Monte Carlo模拟计算的D-统计量（S）。使用此参数，当lookup是FALSE，或当lookup是TRUE和表中的缺失值对应到指定的意义。默认值：10000。
参数：n.repetition
an integer specifying the number of Monte Carlo simulations to perform. This parameter is coordinated with the n.realization parameter. Default: 3.
一个整数，指定执行的Monte Carlo模拟的数量。此参数配合n.realization参数。默认值：3。
参数：significance
a numeric vector of real values on the interval (0,1). Qualitatively the significance is the fraction of times that the linear cumulative energy hypothesis is incorrectly rejected. It is equal to the difference of the distribution probability (p) and unity. Default: c(0.1, 0.05, 0.01).
一个数值向量的真实值的时间间隔（0,1）上。定性的意义是线性累积能量的假设是不正确的拒绝次数的馏分。它是等于差的分布的概率（p）和统一。默认值：c(0.1, 0.05, 0.01)。
参数：tolerance
a numeric real scalar that specifies the amplitude threshold to use in estimating critical D-statistic(s) via the Inclan-Tiao approximation. Setting this parameter to a higher value results in a lesser number of summation terms at the expense of obtaining a less accurate approximation. Default: 1e-6.
一个数字的真正标，Inclan眺近似的估计关键D-统计量（S），在指定的幅度阈值使用。这个参数设置为较高的值的总和计算，在数量较少的费用获得较准确的近似结果。默认值：1e-6。
参数：wavelet
a character string denoting the filter type. Valid only for input not of class wavTransform or wavBoundary. Default: "s8".
一个字符串，表示过滤器的类型。仅适用于输入类wavTransform或wavBoundary。默认值："s8"。
Details
详细信息----------Details----------
An Inclan-Tiao approximation of critical D-statistics is used for sample sizes N >= 128 while a Monte Carlo technique is used for N < 128. For the Monte Carlo technique, the D-statistic for a Gaussian white noise sequence of length N is calculated. This process is repeated n.realization times, forming a distribution of the D-statistic. The critical values corresponding to the significances are calculated a total of n.repetition times, and averaged to form an approximation to the D-statistic(s). Because the Monte Carlo study can be both computationally and memory intensive, it is highly recommended that lookup be set to TRUE, its default value.
Inclan眺近似的关键D-统计样本量用于N >= 128，而蒙特卡罗技术用于N < 128。蒙特卡罗技术，D-统计量的高斯白噪声序列的长度N计算。重复这一过程，n.realization倍，形成的D-统计量的分布的。对应的意义的临界值计算共有n.repetition倍，平均以形成一个近似为D-统计量（）。由于Monte Carlo模拟计算和内存密集型，这是强烈建议，查找被设置为TRUE，它的默认值。
值----------Value----------
an object of class wavVarTest.
对象类wavVarTest。
参考文献----------References----------
D. B. Percival and A. T. Walden, Wavelet Methods for Time Series Analysis, Cambridge University Press, 2000.
参见----------See Also----------
实例----------Examples----------
## perform a homogeneity of variance test for a [＃进行方差齐性检验的]
## DWT decomposition of a long memory process [＃DWT分解的长记忆过程]
## realization [＃实现]
homogeneity <- wavVarTest(fdp045)
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 22:57:50
