hybrid.filter(robfilter)
hybrid.filter()所属R语言包：robfilter
Robust Hybrid Filtering Methods for Univariate Time Series
强大的单变量时间序列的混合滤波方法
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Procedures for robust extraction of low frequency components (the signal) from a univariate time series based on a moving window technique using the median of several one-sided half-window estimates (subfilters) in each step.
鲁棒提取的低频分量（信号）从一个单变量的时间序列基础上的移动窗口技术在每个步骤中使用的中位数的几个片面半窗口估计（的子滤镜）的程序。
用法----------Usage----------
hybrid.filter(y, width, method = "all", minNonNAs=3, extrapolate = TRUE)
参数----------Arguments----------
参数：y
a numeric vector or (univariate) time series object.
一个数值向量或单变量时间序列对象。
参数：width
an odd positive integer (>=3) defining the window width used for fitting.
奇正整数（>=3）用于拟合定义窗口的宽度。
参数：method
a (vector of) character string(s) containing the method(s) to be used for the estimation of the signal level.<br> It is possible to specify any combination of "MED", "RM", "MEAN", FMH, "PFMH", "CFMH", "MH", "PRMH", "CRMH", "MMH", "PRMMH", "CRMMH", and "all" (for all of the above). Default is method="all". For a detailed description see the section "Methods" below.
字符串（矢量）（）含有（s）到用于估计的信号电平的方法。参考指定的任何组合"MED"，"RM"，它是可能的"MEAN"，FMH，"PFMH"，"CFMH"，"MH"，"PRMH"，"CRMH"，"MMH"，"PRMMH"，"CRMMH"和"all"（上述）。默认是method="all"。的详细说明，请参阅“一节中方法”。
参数：minNonNAs
a positive integer defining the minimum number of non-missing observations within each window (half) which is required for a "sensible" estimation. Default: if a window (half) contains less than minNonNAs = 3 observations an NA is returned (for that subfilter).
限定非缺失观测的最小数目的一个正整数，每个窗口内（一半），这是需要的一个明智估计。默认值：如果一个窗口（半）包含小于minNonNAs = 3NA返回的观察（该子滤镜）。
参数：extrapolate
a logical indicating whether the level estimations should be extrapolated to the edges of the time series. The extrapolation extends the first estimated value to the first time in the first window and the last estimated value to the last time in the last time window. Default is extrapolate=TRUE.
一个逻辑指示的水平估计是否应当推断的时间序列的边缘。该外推法延伸的第一估计值的第一时间的第一窗口中，在过去的时间窗口中的最后时间和最后的估计值。默认是extrapolate=TRUE。
Details
详细信息----------Details----------
hybrid.filter is suitable for extracting low frequency components (the signal) from a time series which may be contaminated with outliers and can contain level shifts or local extremes. For this, moving window techniques are applied.
hybrid.filter是适合于从时间序列中提取的低频分量（信号）与离群值可能被污染，并且可以包含电平漂移或本地极端。对于这一点，移动窗口技术被应用。
Within each time window several subfilters are applied to half-windows (left and right of the centre); the final signal level in the centre of the time window is then estimated by the median of the subfilter outputs.
在每个时间窗口中的几个子滤镜被施加到半窗口（左和右的中心）的最终信号电平中的时间窗口的中心，然后估计的子滤波器输出的中位数。
For the subfilters, both, location-based and regression-based method are available, the former applying means or medians and the idea of a locally constant signal value, the latter using ordinary least squares (LS) regression or Siegel's (1982) repeated median (RM) and the idea of an underlying locally linear trend.
对于的子滤镜，都，位置，基础和基于回归的方法是可用的，前者运用的手段或中位数和一个局部恒定的信号值的概念，后者使用普通最小平方（LS）回归或Siegel的（1982）重复位数（RM）和局部线性趋势的一个基本的想法。
The methods should be chosen based on an a-priori guess of the underlying signal and the data quality. Location based methods (MED, MEAN, FMH, MH, MMH) are recommended in case of a locally (piecewise) constant signal. Regression based and predictive approaches (RM, PFMH, PRMH, PRMMH) in case of locally linear monotone trends. The combined filters (CFMH, CRMH, CRMMH) can be seen as a compromise, but are computationally somewhat more expensive and may be inferior to the predictive filters during steep trends.
应选择的方法，基于先验猜底层信号和数据质量。基于位置的方法（MED，MEAN，FMH，MH，MMH），建议在本地（分段）恒定的信号的情况下。基于回归和预测方法（RM，PFMH，PRMH，PRMMH）的情况下局部线性单调趋势。组合过滤器（CFMH，CRMH，CRMMH）可以看作是一种妥协，但计算较为昂贵，并可能不如在陡峭的趋势预测滤波器。
The approaches based on the median and RM are robust alternatives to the (in Gaussian samples) more efficient mean and least squares methods. The hybrid filters preserve shifts and local extremes much better than MED, MEAN or RM for the price of decreased robustness and / or Gaussian efficiency.
到的（高斯样品）更有效的均值和最小二乘法的基础上，中位数和RM的方法是可靠的替代品。混合滤波器保持的变化和极端远优于MED，MEAN或RM价格下降的鲁棒性和/高斯效率的。
值----------Value----------
hybrid.filter returns an object of class hybrid.filter. An object of class hybrid.filter is a list containing the following components:
hybrid.filter返回一个对象类hybrid.filter。一个对象的类hybrid.filter的是一个列表，其中包含以下组件：
参数：level
a data frame containing the signal level extracted by the filter(s) specified in method.
一个数据框由过滤器（）中指定的method萃取含有的信号电平。
参数：slope
a data frame (possibly) containing RM, RM.left, RM.right, LS.left and LS.right: the slope estimated by Repeated Median regression in the whole window (for method="RM") or in the left and right window half (for any method in "PRMH", "CRMH", "PRMMH" and "CRMMH") or the least squares slope estimated from the left and right window half (for any method in "PRFMH" or "CFMH").<br> Only those slopes are returned which are required by the filters specified in method. If only location-based filters are applied (i.e. "MED", "MEAN", "FMH", "MH" and /or "MMH") NULL is returned for the slope.
一个数据框（可能）RM，RM.left，RM.right，LS.left和LS.right：在整个窗口的反复中位数回归的斜率估计（为method="RM"），或在左，右窗口的一半（任何method中"PRMH"，"CRMH"，"PRMMH"和"CRMMH"）或至少广场斜率估计从左边和右边窗口的一半（任何method中"PRFMH"或"CFMH"）。参考，只有那些斜坡所需要的过滤器中指定的<返回X>。如果只基于位置的过滤器（即method，"MED"，"MEAN"，"FMH"和/或"MH"）"MMH"返回NULL。
In addition, the original input time series is returned as list member y, and the settings used for the analysis are returned as the list members width, method and extrapolate.
此外，原有的输入时间序列的形式返回列表成员y，用于分析和设置列表成员返回width，method和extrapolate。
Application of the function plot to an object of class hybrid.filter returns a plot showing the original time series with the filtered output.
应用的功能plot对象的类hybrid.filter返回一个图，显示的原始时间序列的过滤输出。
方法----------Methods----------
The following methods are available as method for signal extraction.
以下方法可作为method信号提取。
Filters applying only one location or regression estimate to the whole window of length width and taking the location (in the centre of the time window) as final signal level estimate:
过滤器只在一个位置或回归估计施加到整个窗口的长度width，并考虑最终信号电平的估计（在中心的时间窗口的位置）作为：
MED ordinary running median filter.
MED普通运行中值滤波器。
MEAN ordinary moving average filter.
MEAN普通移动平均滤波器。
RM ordinary repeated median filter. <br>
RM普通的重复中值滤波器。参考
Filters applying several subfilters within one window, taking the median of the values listed below as the final signal level estimate:
过滤器在一个窗口中，将几个子滤镜下面所列的值作为最终信号电平估计的中位数：
FMH FIR median hybrid filter. <br>
FMH的FIR中位数混合过滤器。参考
PFMH predictive FMH filter. <br> Uses half-window least squares regression and the central
PFMH预测FMH过滤器。参考使用半窗最小二乘回归和中央
CFMH combined FMH filter.<br> Uses half-window averages, half-window least squares
CFMH：结合FMH过滤器。<BR>使用半窗口的平均值，半窗最小二乘
MH median hybrid filter. <br>
MH值混合滤波器。参考
PRMH predictive repeated median hybrid filter. <br> Uses half-window repeated median regression and the
PRMH预测重复值混合过滤器。参考重复使用半窗口中位数回归和
CRMH combined repeated median hybrid filter. <br> Uses half-window medians, half-window repeated median
CRMH相结合重复值混合过滤器。参考使用半窗口中位数，一半窗口重复中位数
MMH median/median hybrid filter. <br> Uses half-window medians and the median of all
MMH的中位数/平均混合滤波器。参考使用半窗口中位数和中位数
PRMMH predictive repeated median/median filter. <br> Uses half-window repeated median regression and the
PRMMH预测重复位数/中值滤波。参考重复使用半窗口中位数回归和
CRMMH combined repeated median/median filter. <br> Uses half-window medians, half-window repeated median
CRMMH加起来重复位数/中值滤波。参考使用半窗口中位数，一半窗口重复中位数
注意----------Note----------
Missing values are treated by omitting them and thus by reducing the corresponding window width. <br> The hybrid.filter function only offers filters for signal extraction delayed by (width+1)/2 time units, in contrast to other filters available from the robfilter package which also offer online time series analysis without time delay.
缺少的值被视为通过省略他们的，从而通过降低相应的窗口宽度。参考hybrid.filter功能仅提供过滤器提取信号延迟（width+1）/ 2个时间单位，在其他过滤器，可从robfilter包还提供在线时间序列分析没有时间延迟。
（作者）----------Author(s)----------
Roland Fried and Karen Schettlinger
参考文献----------References----------
Repeated Median and Hybrid Filters, Computational Statistics \& Data Analysis 50, 2313-2338. <br> (earlier version: http://www.sfb475.uni-dortmund.de/berichte/tr10-04.ps)
Robust Filters for Intensive Care Monitoring: Beyond the Running Median, Biomedizinische Technik 51(2), 49-56.
参见----------See Also----------
robreg.filter, robust.filter, dw.filter, wrm.filter.
robreg.filter，robust.filter，dw.filter，wrm.filter。
实例----------Examples----------
# Generate random time series:[生成随机时间序列：]
y <- cumsum(runif(500)) - .5*(1:500)
# Add jumps:[添加跳转：]
y[200:500] <- y[200:500] + 5
y[400:500] <- y[400:500] - 7
# Add noise:[添加噪声：]
n <- sample(1:500, 30)
y[n] <- y[n] + rnorm(30)
# Filtering with all methods:[过滤用一切手段：]
y.hy <- hybrid.filter(y, width=31)
# Plot:[图：]
plot(y.hy)
# Filtering with running median and PRMH only:[过滤与运行中位数和PRMH：]
y2.hy <- hybrid.filter(y, width=31, method=c("MED","PRMH"))
plot(y2.hy)
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注：
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发表于 2012-9-27 21:02:47
