spec.pgram(stats)
spec.pgram()所属R语言包：stats
Estimate Spectral Density of a Time Series by a Smoothed
由平滑时间序列的谱密度估计
译者：生物统计家园网 机器人LoveR
描述----------Description----------
spec.pgram calculates the periodogram using a fast Fourier transform, and optionally smooths the result with a series of modified Daniell smoothers (moving averages giving half weight to the end values).
spec.pgram用快速傅里叶变换计算周期图，可选平滑的一系列改性丹尼尔平滑（均线一半重量的最终值）的结果。
用法----------Usage----------
spec.pgram(x, spans = NULL, kernel, taper = 0.1,
pad = 0, fast = TRUE, demean = FALSE, detrend = TRUE,
plot = TRUE, na.action = na.fail, ...)
参数----------Arguments----------
参数：x
univariate or multivariate time series.
单因素或多元时间序列。
参数：spans
vector of odd integers giving the widths of modified Daniell smoothers to be used to smooth the periodogram.
矢量平滑给改性丹尼尔的宽度奇数的被用来平滑周期图。
参数：kernel
alternatively, a kernel smoother of class "tskernel".
另外，一个内核级的"tskernel"平滑。
参数：taper
specifies the proportion of data to taper. A split cosine bell taper is applied to this proportion of the data at the beginning and end of the series.
指定锥度数据的比例。分割余弦钟锥应用于这个比例的数据系列的开头和结尾。
参数：pad
proportion of data to pad. Zeros are added to the end of the series to increase its length by the proportion pad.
垫的数据比例。零点添加到年底的系列，以增加它的长度比例pad。
参数：fast
logical; if TRUE, pad the series to a highly composite length.
逻辑;如果TRUE，垫一个高度综合的长度系列。
参数：demean
logical. If TRUE, subtract the mean of the series.
逻辑。如果TRUE，减去该系列的平均值。
参数：detrend
logical. If TRUE, remove a linear trend from the series. This will also remove the mean.
逻辑。如果TRUE，删除从一系列的线性趋势。这也将删除的意味。
参数：plot
plot the periodogram?
绘制的周期图？
参数：na.action
NA action function.
NA动作功能。
参数：...
graphical arguments passed to plot.spec.
图形参数传递到plot.spec。
Details
详情----------Details----------
The raw periodogram is not a consistent estimator of the spectral density, but adjacent values are asymptotically independent. Hence a consistent estimator can be derived by smoothing the raw periodogram, assuming that the spectral density is smooth.
原始周期图是一致的谱密度估计，但相邻的值是渐近独立的。平滑原材料周期图，假设谱密度是顺利的，因此可以得出一个一致的估计。
The series will be automatically padded with zeros until the series length is a highly composite number in order to help the Fast Fourier Transform. This is controlled by the fast and not the pad argument.
该系列将自动用零填充，直到序列的长度是一个高度综合的数字，以帮助快速傅立叶变换。这是控制由fast不pad论点。
The periodogram at zero is in theory zero as the mean of the series is removed (but this may be affected by tapering): it is replaced by an interpolation of adjacent values during smoothing, and no value is returned for that frequency.
在零周期图是在理论上为零作为该系列的平均被删除（但是这可能是由尖细的影响）：它被替换在平滑插值相邻值，没有值返回该频率。
值----------Value----------
A list object of class "spec" (see spectrum) with the following additional components:
一个列表对象类"spec"（见spectrum）以下的附加组件：
参数：kernel
The kernel argument, or the kernel constructed from spans.
kernel论点，或从spans构建内核。
参数：df
The distribution of the spectral density estimate can be approximated by a (scaled) chi square distribution with df degrees of freedom.
（规模）智df自由度平方分布谱密度估计的分布可以近似。
参数：bandwidth
The equivalent bandwidth of the kernel smoother as defined by Bloomfield (1976, page 201).
等效带宽的内核由菲尔德定义平滑（1976年，第201页）。
参数：taper
The value of the taper argument.
taper参数值。
参数：pad
The value of the pad argument.
pad参数值。
参数：detrend
The value of the detrend argument.
detrend参数值。
参数：demean
The value of the demean argument.
demean参数值。
The result is returned invisibly if plot is true.
返回结果如果plot是真正的无形。
作者（S）----------Author(s)----------
Originally Martyn Plummer; kernel smoothing by Adrian Trapletti,
synthesis by B.D. Ripley
参考文献----------References----------
Introduction. Wiley.
Methods. Second edition. Springer.
Statistics with S. Fourth edition. Springer. (Especially pp. 392–7.)
参见----------See Also----------
spectrum, spec.taper,
spectrum，spec.taper
举例----------Examples----------
require(graphics)
## Examples from Venables & Ripley[＃例如从维纳布尔斯和里普利]
spectrum(ldeaths)
spectrum(ldeaths, spans = c(3,5))
spectrum(ldeaths, spans = c(5,7))
spectrum(mdeaths, spans = c(3,3))
spectrum(fdeaths, spans = c(3,3))
## bivariate example[＃二元的例子]
mfdeaths.spc <- spec.pgram(ts.union(mdeaths, fdeaths), spans = c(3,3))
# plots marginal spectra: now plot coherency and phase[图边际谱：现在小区的一致性和相位]
plot(mfdeaths.spc, plot.type = "coherency")
plot(mfdeaths.spc, plot.type = "phase")
## now impose a lack of alignment[＃现在并处对齐的缺乏]
mfdeaths.spc <- spec.pgram(ts.intersect(mdeaths, lag(fdeaths, 4)),
spans = c(3,3), plot = FALSE)
plot(mfdeaths.spc, plot.type = "coherency")
plot(mfdeaths.spc, plot.type = "phase")
stocks.spc <- spectrum(EuStockMarkets, kernel("daniell", c(30,50)),
plot = FALSE)
plot(stocks.spc, plot.type = "marginal") # the default type[默认类型]
plot(stocks.spc, plot.type = "coherency")
plot(stocks.spc, plot.type = "phase")
sales.spc <- spectrum(ts.union(BJsales, BJsales.lead),
kernel("modified.daniell", c(5,7)))
plot(sales.spc, plot.type = "coherency")
plot(sales.spc, plot.type = "phase")
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。
|
发表于 2012-2-17 10:16:40
