impSeqRob(rrcovNA)
impSeqRob()所属R语言包：rrcovNA
Robust sequential imputation of missing values
遗漏值的鲁棒顺序归集
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Impute missing multivariate data using robust sequential algorithm
填补缺失多变量数据，利用强大的序贯算法
用法----------Usage----------
impSeqRob(x, alpha=0.9)
参数----------Arguments----------
参数：x
the original incomplete data matrix.
原来不完整的数据矩阵。
参数：alpha
.The default is alpha=0.9.
默认是alpha=0.9。
Details
详细信息----------Details----------
SEQimpute starts from a complete subset of the data set Xc and estimates sequentially the missing values in an incomplete observation, say x*, by minimizing the determinant of the covariance of the augmented data matrix X* = [Xc; x']. Then the observation x* is added to the complete data matrix and the algorithm continues with the next observation with missing values. Since SEQimpute uses the sample mean and covariance matrix it will be vulnerable to the influence of outliers and it is improved by plugging in robust estimators of location and scatter. One possible solution is to use the outlyingness measure as proposed by Stahel (1981) and Donoho (1982) and successfully used for outlier identification in Hubert et al. (2005). We can compute the outlyingness measure for the complete observations only but once an incomplete observation is imputed (sequentially) we could compute the outlyingness measure for it too and use it to decide if this observation is an outlier or not. If the outlyingness measure does not exceed a predefined threshold the observation is included in the further steps of the algorithm.
SEQimpute从一个完整的数据集的子集Xc，估计在一个不完整的观察顺序的遗漏值，说X *，增强数据的协方差矩阵的行列式最小化X * = [ Xc的等，x]。然后观察x *是添加到完整的数据矩阵和算法继续具有缺失值的下一个观察。由于SEQimpute使用的样本均值和协方差矩阵，这将是脆弱的离群值的影响，提高了插入的位置和分散的稳健估计的。一个可能的解决方案是使用的outlyingness Stahel（1981）和Donoho（1982）所提出的措施，并成功地用于在休伯特等的异常值识别，。 （2005年）。获得完整的观察，我们可以计算出outlyingness措施只是一个不完整的观察一次归于（依次），我们可以计算outlyingness措施它太，并用它来决定如果这个观察是一个离群或没有。观察算法的进一步的步骤包括在，如果outlyingness措施不超过预定义的阈值。
值----------Value----------
a matrix of the same form as x, but with all missing values filled in sequentially.
矩阵x，但所有缺失值填充的顺序相同的形式。
参考文献----------References----------
Sequential imputation for missing values. Computational Biology and Chemistry, bold31, 320–327. K. Vanden Branden and S. Verboven (2009). Robust Data Imputation. Computational Biology and Chemistry, 33, 7–13.
实例----------Examples----------
data(bush10)
impSeqRob(bush10) # impute squentially missing data[推诿squentially丢失的数据]
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 2012-9-28 20:40:08
