waveclock(waveclock)
waveclock()所属R语言包：waveclock
Reconstruction of the modal frequencies in a time series using continuous wavelet transformation and the "crazy climbers" algorithm
重建时间序列中的模态频率使用连续小波变换的“疯狂攀登者”的算法
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function can be used to reconstruct the modal frequencies in a time series such as cycling cell luminescence data. First the continuous wavelet transform is calculated using the (complex-valued) Morlet wavelet. Next, the modal frequencies are identified from the time-frequency decomposition using the "crazy climbers" algorithm from package Rwave.
这个功能可以被用来重建，如循环单元发光数据在时间序列中的模态频率。首先，连续小波变换计算使用Morlet小波（复值）。接下来，模态频率识别的时频分解的“疯狂攀登者”的算法，从包装Rwave。
用法----------Usage----------
waveclock( x, period = c( 6, 48 ), time.limits = NULL, extend = "reflect",
noctave = NULL, nvoice = 96, mask.coi = TRUE,
crc.args = list( seed = 0, nbclimb = 50 ),
cfamily.args = list( ptile = 0.005, bstep = 5, nbchain = 400 ),
crcrec.args = list( compr = 3, epsilon = 0, para = 3, plot = FALSE ),
xlab = "Time (h)", ylab = "Period (h)", png = NULL,
color.palette = heat.colors, mode.col = "green", mode.lty = "solid",
mode.lwd = 2, ... )
参数----------Arguments----------
参数：x
Numeric or complex vector or time series. Input signal (possibly complex-valued).
数字或复杂的矢量或时间序列。输入信号（可能是复数）。
参数：period
Numeric vector. Range defining lower and upper period limits. The default values may be useful for detecting circadian rhythmicity in data with time units measured in hours
数字矢量。范围限定下部和上部的期间限制。默认值可能是有用的，用于检测数据的昼夜节律中的时间单位以小时计，
参数：time.limits
Numeric vector. Time range for truncation of series
数字矢量。系列截断的时间范围
参数：extend
NULL or character string. Ameliorate edge effects by reflecting data series at time limits or repeating the time series. Must be NULL or an abbreviation of "reflect" or "repeat".
NULL或字符串。改善边缘效应的反映时间限制的数据系列或重复的时间序列。必须为NULL或简称“反映”或“重复”。
参数：mask.coi
Logical. Is the "cone of influence" masked from the output?
逻辑。是“影响锥”屏蔽输出？
参数：noctave
Numeric. Number of powers of 2 for the scale variable in the wavelet decomposition. Defaults to the maximum number possible
数字。规模可变的小波分解为2的幂数。默认的最大数量可能
参数：nvoice
Numeric. Number of scales in each octave (i.e., between two consecutive powers of 2)
数字。在每个倍频数比例（即在两个连续的2的幂）
参数：crc.args
List. Arguments provided to "crazy climbers" function crc
列表。提供的参数“疯狂攀登者”函数“crc
参数：cfamily.args
List. Arguments provided to chaining function cfamily
列表。参数提供到链接功能cfamily
参数：crcrec.args
List. Arguments provided to modal frequency reconstruction function crcrec
列表。提供的参数模态频率重建功能crcrec
参数：xlab
Character string. x axis label for plot of continuous wavelet transform scalogram
字符的字符串。 x轴标签图连续小波变换尺度图
参数：ylab
Character string. y axis label for plot of continuous wavelet transform scalogram
字符的字符串。 y轴标签图连续小波变换尺度图
参数：png
NULL (default) or character string. Name of png filename for plot output. The default value plots to the default device
NULL（默认）或字符串。名称图输出的PNG文件名。默认值图的默认设备
参数：color.palette
Color palette function used in scalogram plot
尺度谱图中使用的调色板功能
参数：mode.col
Color of line marking modal frequency
颜色的标线模态频率
参数：mode.lty
Type of line marking modal frequency
标线模态频率类型
参数：mode.lwd
Width of line marking modal frequency
标线模态频率宽度
参数：...
Additional parameters passed to filled.contour plot function
额外的参数传递给filled.contour绘图功能
Details
详细信息----------Details----------
original.signal Contains the original signal provided to the function.
original.signal包含原始信号，提供给函数。
modified.signal Contains the modified signal (after truncation and reflection) used in the analysis.
modified.signal包含在分析中使用的修改的信号（后截断和反射）。
cwt Contains the (complex) values of the continuous wavelet transform of the input signal. Since Morlet's wavelet is not strictly speaking a wavelet (it is not of vanishing integral), artifacts may occur for certain signals.
百磅包含（复杂）的值的输入信号的连续小波变换。由于Morlet子的小波不是严格意义上的一个的小波（它不是消失积分），的文物可能发生某些信号。
crc Modal frequencies identified by "crazy climbers" algorithm
由“疯狂攀登者”算法的CRC模态频率
cfamily Output of procedure to remove short discontinuities in the modal frequencies
cfamily输出的程序，以消除短期的不连续性的模态频率
modes Matrix containing information about the modal frequencies. The first column gives an index for the modal frequency that corresponds to the row number in cfamily\$chain. The next three columns identify the median voice of the modal signal, excluding the cone of influence (region subject to edge effects). Each voice corresponds to a range of periods: columns 2, 3, and 4 give the midpoint, lower limit, and upper limit of periods under the median voice. The next three columns summarize the period lengths of each mode by averaging over time, again excluding the cone of influence. Columns 5, 6, and 7 give the mean of the midpoints, lower limits, and upper limits. The last column gives the variance of the reconstructed wave. The rows correspond to the modes identified. The final row gives the the statistics for all the modal frequencies combined and can be useful when a single mode is split into segments, as in the example.
模式矩阵包含的模态频率的信息。第一列给出了一个指数为对应cfamily \ $链中的行数的模态频率。接下来的三个列的模态信号识别中位数的声音，但不包括影响锥（区边缘效应）。每个语音对应的周期的范围内：2，第3，和第4列给出的中点，下限，上限的周期下的中位数的声音。接下来的三个列总结了每种模式的周期长度均随着时间的推移，，再次不包括锥的影响。列5，6，和7中得到的平均值的中点，下限和上限。最后一列给出了重建波的方差。的行对应的模式的方法。最后一行给出了所有的模态频率结合的统计信息时，可以使用一个单一的模式被分成段，如示例中所示。
rec Modal frequencies reconstructed as time series.
建议模态频率的时间序列重建。
per The instantaneous period of the modes, as measured by the logarithmic midpoint of the wavelet scale, or 0 outside the cone of influence; NA within the cone of influence.
元瞬时周期的模式中，所测得的对数的中点小波规模，或0以外的影响锥; NA影响锥内。
amp The instantaneous amplitude of the modes as measured by the modulus of the Morlet wavelet at each time point, or 0 outside the cone of influence; NA within the cone of influence.
安培Morlet小波的模量的测定通过在每个时间点，或0以外的影响锥的模式;影响锥内的NA的瞬时幅值。
phase The instantaneous phase of the modes, as measured by the argument of the Morlet wavelet at each time point, or 0 outside the cone of influence; NA within the cone of influence.
锥形范围内的影响力逐步的瞬时相位的模式，在每个时间点，或0外锥面的影响的说法，Morlet小波作为衡量; NA。
mask Voice numbers for the modal frequencies, or 0 outside the cone of influence; NA within the cone of influence.
面具语音号码的模态频率，或0外锥面的影响; NA锥形范围内的影响力。
（作者）----------Author(s)----------
T.S.Price
参考文献----------References----------
"Practical Time-Frequency Analysis: Gabor and Wavelet Transforms with an Implementation in S", by Rene Carmona, Wen L. Hwang and Bruno Torresani, Academic Press, 1998. http://sgdp.iop.kcl.ac.uk/tprice/software.html
参见----------See Also----------
See cwt for the continuous wavelet transform, crc and cfamily for the estimation of modal frequencies from the continuous wavelet transform, crcrec for the reconstruction of the modal frequencies, and waveclock.auto to run waveclock in another instance of R.
见cwt的连续小波变换，crc和cfamily的估计模态频率的连续小波变换，crcrec重建的模态频率，和 waveclock.auto运行waveclock的另一个实例，R.
实例----------Examples----------
set.seed( 1 )
freq <- 6 # data point every 10 minutes[数据点，每10分钟]
T <- 24 * 5 * freq
t <- ( 0:T ) / freq
# models an initial 'spike' and slow background trend[模型的最初的“秒杀”和缓慢的背景趋势]
spike <- 0.5 * dgamma( t / 24, 2, 10 )
trend <- rowSums( poly( t, 2 ) %*% rnorm( 2 ) )
background <- spike + trend
# exponentially damped circadian signal with random phase[指数衰减昼夜信号与随机相]
amplitude <- sqrt( 2 ) * exp( -t / ( 24 * 2 ) )
period <- 24
phase <- runif( 1 ) * 2 * pi
signal <- amplitude * sin( t / period * 2 * pi + phase )
# Gaussian noise[高斯噪声]
noise <- 0.15 * rnorm( T + 1 )
# simulated luminescence trace[模拟发光一丝的]
luminescence.trace <- ts( signal + background + noise, start = 0, freq = freq )
plot( luminescence.trace )
# wavelet analysis[小波分析]
result <- waveclock( luminescence.trace )
result$modes
reconstructed.trace <- ts( rowSums( result$rec, na.rm = TRUE ), start = 0, freq = freq )
# plot reconstructed wave[图重建的波]
# code not run[代码无法运行]
##plot( luminescence.trace )[＃图（luminescence.trace）]
##lines( reconstructed.trace, col = 2 )[＃线（reconstructed.trace，山坳= 2）]
##abline( h = 0, lty = 2, col = 2 )[＃abline（h = 0时，LTY = 2，列= 2）]
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 16:53:36
