rowRoblox and colRoblox(RobLox)
rowRoblox and colRoblox()所属R语言包：RobLox
Optimally robust estimation for location and/or scale
最优稳健估计的位置和/或规模
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The functions rowRoblox and colRoblox compute optimally robust estimates for normal location und/or scale and (convex) contamination neighborhoods. The definition of these estimators can be found in Rieder (1994) or Kohl (2005), respectively.
的功能rowRoblox和colRoblox计算最佳强大的估计正常的位置UND /或规模（凸）污染居民区。里德尔（1994）或科尔（2005）中可以找到，这些估计的定义分别。
用法----------Usage----------
rowRoblox(x, mean, sd, eps, eps.lower, eps.upper, initial.est, k = 1L,
fsCor = TRUE, mad0 = 1e-4, na.rm = TRUE)
colRoblox(x, mean, sd, eps, eps.lower, eps.upper, initial.est, k = 1L,
fsCor = TRUE, mad0 = 1e-4, na.rm = TRUE)
参数----------Arguments----------
参数：x
matrix or data.frame of (numeric) data values.
矩阵或数据框（数字）的数据值。
参数：mean
specified mean. See details below.
指定的意思。详见下文。
参数：sd
specified standard deviation which has to be positive. See also details below.
指定的标准偏差必须为正数。请参阅下面详细说明。
参数：eps
positive real (0 < eps <= 0.5): amount of gross errors. See details below.
正实数（0 <eps<= 0.5）：量的严重错误。详见下文。
参数：eps.lower
positive real (0 <= eps.lower <= eps.upper): lower bound for the amount of gross errors. See details below.
正实（0 <=eps.lower<=eps.upper）：下限量的严重错误。详见下文。
参数：eps.upper
positive real (eps.lower <= eps.upper <= 0.5): upper bound for the amount of gross errors. See details below.
正实（eps.lower<=eps.upper<= 0.5）：上界为量的严重错误。详见下文。
参数：initial.est
initial estimate for mean and/or sd. If missing median and/or MAD are used.
初步估计mean和/或sd。如果没有中位数和/或MAD使用。
参数：k
positive integer. k-step is used to compute the optimally robust estimator.
正整数。 k步被用于计算最优鲁棒估计。
参数：fsCor
logical: perform finite-sample correction. See function finiteSampleCorrection.
逻辑：执行有限样本校正。请参阅功能finiteSampleCorrection。
参数：mad0
scale estimate used if computed MAD is equal to zero
使用的规模估计，如果计算MAD等于零
参数：na.rm
logical: if TRUE, the estimator is evaluated at complete.cases(x).
逻辑：如果TRUE，估计是评价complete.cases(x)。
Details
详细信息----------Details----------
Computes the optimally robust estimator for location with scale specified, scale with location specified, or both if neither is specified. The computation uses a k-step construction with an appropriate initial estimate for location or scale or location and scale, respectively. Valid candidates are e.g. median and/or MAD (default) as well as Kolmogorov(-Smirnov) or Cram\'er von Mises minimum distance estimators; cf. Rieder (1994) and Kohl (2005). In case package Biobase from Bioconductor is installed as is suggested, median and/or MAD are computed using function rowMedians.
计算最优鲁棒估计为指定的位置，规模，规模与指定的位置，如果没有指定。的计算采用了适当的位置或规模或地点和规模初步估计，k步建设，。有效候选人是如中位数和/或的MAD（默认）以及柯尔莫哥洛夫（斯米尔诺夫）或“补习\呃·冯·米塞斯的最小距离估计;比照。里德尔（1994）和科尔（2005年）。从Bioconductor包BIOBASE的安装建议的那样，中位数和/或MAD计算使用功能rowMedians。
These functions are optimized for the situation where one has a matrix and wants to compute the optimally robust estimator for every row, respectively column of this matrix. In particular, the amount of cross errors is assumed to be constant for all rows, respectively columns.
这些函数进行优化的情况下，一个有一个矩阵，并要计算最优鲁棒估计分别为每一行，列此矩阵。特别是，交叉的误差量被假定为常数的所有的行，分别列。
If the amount of gross errors (contamination) is known, it can be specified by eps. The radius of the corresponding infinitesimal contamination neighborhood is obtained by multiplying eps by the square root of the sample size.
如果严重的错误（污染）的量是已知的，它可以指定eps。是通过以下方式获得相应的无穷小的污染附近的半径乘以eps的样本大小的平方根。
If the amount of gross errors (contamination) is unknown, try to find a rough estimate for the amount of gross errors, such that it lies between eps.lower and eps.upper.
如果是未知的总误差量（污染），试图找到一个粗略的估计量的严重错误，它位于之间eps.lower和eps.upper。
In case eps.lower is specified and eps.upper is missing, eps.upper is set to 0.5. In case eps.upper is specified and eps.lower is missing, eps.lower is set to 0.
有时eps.lower指定eps.upper缺少，eps.upper被设定为0.5。有时eps.upper指定eps.lower缺少，eps.lower被设置为0。
If neither eps nor eps.lower and/or eps.upper is specified, eps.lower and eps.upper are set to 0 and 0.5, respectively.
如果既不eps也不eps.lower和/或eps.upper指定，eps.lower和eps.upper被设置为0和0.5，分别。
If eps is missing, the radius-minimax estimator in sense of Rieder et al. (2008), respectively Section 2.2 of Kohl (2005) is returned.
eps如果丢失，半径极小极大估计的感Rieder等人。 （2008年），科尔（2005年）第2.2节回来了。
In case of location, respectively scale one additionally has to specify sd, respectively mean where sd and mean can be a single number, i.e., identical for all rows, respectively columns, or a vector with length identical to the number of rows, respectively columns.
的位置，规模，另外有指定sd，mean其中sd和mean可以是一个单一的数字，即，相同的所有行，分别列，或一个矢量，其长度相同的行数，分别列。
For sample size <= 2, median and/or MAD are used for estimation.
样本大小<= 2，中位数和/或MAD用于估计。
If eps = 0, mean and/or sd are computed.
如果eps = 0，平均值和/或标准差来计算。
值----------Value----------
Object of class "kStepEstimate".
对象类"kStepEstimate"。
（作者）----------Author(s)----------
Matthias Kohl <a href="mailto:Matthias.Kohl@stamats.de">Matthias.Kohl@stamats.de</a>
参考文献----------References----------
Kohl, M. (2005) Numerical Contributions to the Asymptotic Theory of Robustness. Bayreuth: Dissertation.
Rieder, H. (1994) Robust Asymptotic Statistics. New York: Springer.
Rieder, H., Kohl, M. and Ruckdeschel, P. (2008) The Costs of not Knowing the Radius. Statistical Methods and Applications 17(1) 13-40. Extended version: http://www.stamats.de/RRlong.pdf
参见----------See Also----------
roblox, kStepEstimate-class
roblox，kStepEstimate-class
实例----------Examples----------
ind <- rbinom(200, size=1, prob=0.05)
X <- matrix(rnorm(200, mean=ind*3, sd=(1-ind) + ind*9), nrow = 2)
rowRoblox(X)
rowRoblox(X, k = 3)
rowRoblox(X, eps = 0.05)
rowRoblox(X, eps = 0.05, k = 3)
X1 <- t(X)
colRoblox(X1)
colRoblox(X1, k = 3)
colRoblox(X1, eps = 0.05)
colRoblox(X1, eps = 0.05, k = 3)
X2 <- rbind(rnorm(100, mean = -2, sd = 3), rnorm(100, mean = -1, sd = 4))
rowRoblox(X2, sd = c(3, 4))
rowRoblox(X2, eps = 0.03, sd = c(3, 4))
rowRoblox(X2, sd = c(3, 4), k = 4)
rowRoblox(X2, eps = 0.03, sd = c(3, 4), k = 4)
X3 <- cbind(rnorm(100, mean = -2, sd = 3), rnorm(100, mean = 1, sd = 2))
colRoblox(X3, mean = c(-2, 1))
colRoblox(X3, eps = 0.02, mean = c(-2, 1))
colRoblox(X3, mean = c(-2, 1), k = 4)
colRoblox(X3, eps = 0.02, mean = c(-2, 1), k = 4)
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注：
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发表于 2012-9-27 21:10:25
