roblox(RobLox)
roblox()所属R语言包：RobLox
Optimally robust estimator for location and/or scale
最优鲁棒估计的位置和/或规模
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The function roblox computes the optimally robust estimator and corresponding IC 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roblox计算最优鲁棒估计和相应的IC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----------
roblox(x, mean, sd, eps, eps.lower, eps.upper, initial.est, k = 1L,
fsCor = TRUE, returnIC = FALSE, mad0 = 1e-4, na.rm = TRUE)
参数----------Arguments----------
参数：x
vector x of data values, may also be a matrix or data.frame with one row, respectively one column/(numeric) variable.
向量x的数据值，也可以是一个带有一个行的矩阵或数据框，分别为一列/（数值）变量。
参数：mean
specified mean.
指定的意思。
参数：sd
specified standard deviation which has to be positive.
指定的标准偏差必须为正数。
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：returnIC
logical: should IC be returned. See details below.
逻辑：IC被退回。详见下文。
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 von Mises minimum distance estimators; cf. Rieder (1994) and Kohl (2005).
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“冯·米塞斯的最小距离估计;比照。里德尔（1994）和科尔（2005年）。
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 have to be a single number.
的位置，规模的另外指定sd，mean其中sd和mean必须是单数。
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. In this situation it's better to use function MLEstimator.
如果eps = 0，平均值和/或标准差来计算。在这种情况下，最好使用功能MLEstimator。
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----------
ContIC-class, rlOptIC, rsOptIC, rlsOptIC.AL, kStepEstimate-class,
ContIC-class，rlOptIC，rsOptIC，rlsOptIC.AL，kStepEstimate-class，
实例----------Examples----------
ind <- rbinom(100, size=1, prob=0.05)
x <- rnorm(100, mean=ind*3, sd=(1-ind) + ind*9)
## amount of gross errors known[＃已知的严重错误]
res1 <- roblox(x, eps = 0.05, returnIC = TRUE)
estimate(res1)
confint(res1)
confint(res1, method = symmetricBias())
pIC(res1)
checkIC(pIC(res1))
Risks(pIC(res1))
Infos(pIC(res1))
plot(pIC(res1))
infoPlot(pIC(res1))
## amount of gross errors unknown[＃未知量的严重错误]
res2 <- roblox(x, eps.lower = 0.01, eps.upper = 0.1, returnIC = TRUE)
estimate(res2)
confint(res2)
confint(res2, method = symmetricBias())
pIC(res2)
checkIC(pIC(res2))
Risks(pIC(res2))
Infos(pIC(res2))
plot(pIC(res2))
infoPlot(pIC(res2))
## estimator comparison[＃估计比较]
# classical optimal (non-robust)[古典最佳（不健壮的）]
c(mean(x), sd(x))
# most robust[最强大的]
c(median(x), mad(x))
# optimally robust (amount of gross errors known)[最佳的鲁棒性（已知的严重的错误量）]
estimate(res1)
# optimally robust (amount of gross errors unknown)[最佳的鲁棒性（未知量的严重错误）]
estimate(res2)
# Kolmogorov(-Smirnov) minimum distance estimator (robust)[柯尔莫哥洛夫 - 斯米尔诺夫的最小距离估计（健壮）]
(ks.est <- MDEstimator(x, ParamFamily = NormLocationScaleFamily()))
# optimally robust (amount of gross errors known)[最佳的鲁棒性（已知的严重的错误量）]
roblox(x, eps = 0.05, initial.est = estimate(ks.est))
# Cramer von Mises minimum distance estimator (robust)[克莱姆·冯·米塞斯的最小距离估计（强大）]
(CvM.est <- MDEstimator(x, ParamFamily = NormLocationScaleFamily(), distance = CvMDist))
# optimally robust (amount of gross errors known)[最佳的鲁棒性（已知的严重的错误量）]
roblox(x, eps = 0.05, initial.est = estimate(CvM.est))
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。
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发表于 2012-9-27 21:10:13
