getIneffDiff(ROptEst)
getIneffDiff()所属R语言包：ROptEst
Generic Function for the Computation of Inefficiency Differences
通用功能的低效率差异的计算
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Generic function for the computation of inefficiency differencies. This function is rarely called directly. It is used to compute the radius minimax IC and the least favorable radius.
通用功能计算效率低下differencies。很少直接调用此函数。它被用来计算的半径极小极大IC和半径至少有利。
用法----------Usage----------
getIneffDiff(radius, L2Fam, neighbor, risk, ...)
## S4 method for signature 'numeric,L2ParamFamily,UncondNeighborhood,asMSE'
getIneffDiff(
radius, L2Fam, neighbor, risk, loRad, upRad, loRisk, upRisk,
z.start = NULL, A.start = NULL, upper.b = NULL, lower.b = NULL,
OptOrIter = "iterate", MaxIter, eps, warn, loNorm = NULL, upNorm = NULL,
verbose = NULL, ...)
参数----------Arguments----------
参数：radius
neighborhood radius.
邻域半径。
参数：L2Fam
L2-differentiable family of probability measures.
L2-微家庭的概率措施。
参数：neighbor
object of class "Neighborhood".
对象类"Neighborhood"。
参数：risk
object of class "RiskType".
对象类"RiskType"。
参数：loRad
the lower end point of the interval to be searched.
要搜索的下端的间隔的点。
参数：upRad
the upper end point of the interval to be searched.
的上端部的间隔的点进行搜索。
参数：loRisk
the risk at the lower end point of the interval.
在其下端的间隔的点的风险。
参数：upRisk
the risk at the upper end point of the interval.
在其上端的间隔点的风险。
参数：z.start
initial value for the centering constant.
定心常数的初始值。
参数：A.start
initial value for the standardizing matrix.
标准化矩阵的初始值。
参数：upper.b
upper bound for the optimal clipping bound.
上界的最佳剪辑约束。
参数：lower.b
lower bound for the optimal clipping bound.
下界的最佳剪辑约束。
参数：OptOrIter
character; which method to be used for determining Lagrange multipliers A and a: if (partially) matched to "optimize", getLagrangeMultByOptim is used; otherwise: by default, or if matched to "iterate" or to "doubleiterate", getLagrangeMultByIter is used. More specifically, when using getLagrangeMultByIter, and if argument risk is of class "asGRisk", by default and if matched to "iterate" we use only one (inner) iteration, if matched to "doubleiterate" we use up to Maxiter (inner) iterations.
方法用于确定拉格朗日乘子的性格;A和a：如果（部分）匹配"optimize"，getLagrangeMultByOptim使用，否则默认情况下，或者如果相匹配的"iterate"或"doubleiterate"，getLagrangeMultByIter使用。更具体地，使用时：getLagrangeMultByIter，如果参数risk是类"asGRisk"，缺省情况下，和如果"iterate"匹配我们只使用一个（内）迭代，如果匹配"doubleiterate"的“我们使用Maxiter（内部）迭代。
参数：MaxIter
the maximum number of iterations
最大迭代次数
参数：eps
the desired accuracy (convergence tolerance).
所需的精度（收敛宽容）。
参数：warn
logical: print warnings.
逻辑：打印警告。
参数：loNorm
object of class "NormType"; used in selfstandardization to evaluate the bias of the current IC in the norm of the lower bound
类的对象"NormType";在selfstandardization使用评估的偏压电流IC中的范数的低约束
参数：upNorm
object of class "NormType"; used in selfstandardization to evaluate the bias of the current IC in the norm of the upper bound
对象类"NormType";在selfstandardization使用评估的偏压电流IC中的范数的上限的
参数：verbose
logical: if TRUE, some messages are printed
逻辑：如果TRUE，一些消息都印
参数：...
further arguments to be passed on to getInfRobIC
进一步的参数被传递给getInfRobIC
值----------Value----------
The inefficieny difference between the left and the right margin of a given radius interval is computed.
之间的左，右边界的一个给定的半径间隔inefficieny差异计算。
方法----------Methods----------
radius = "numeric", L2Fam = "L2ParamFamily", neighbor = "UncondNeighborhood", risk = "asMSE": computes difference of asymptotic MSE–inefficiency for the boundaries of a given radius interval.
半径=“数字”，L2Fam =“L2ParamFamily”，邻居=“UncondNeighborhood”风险=“asMSE”的：计算一个给定的半径间隔的边界渐近MSE-低效率的差异。
（作者）----------Author(s)----------
Matthias Kohl <a href="mailto:Matthias.Kohl@stamats.de">Matthias.Kohl@stamats.de</a>
参考文献----------References----------
Rieder, H., Kohl, M. and Ruckdeschel, P. (2008) The Costs of not Knowing the Radius. Statistical Methods and Applications, 17(1) 13-40.
Rieder, H., Kohl, M. and Ruckdeschel, P. (2001) The Costs of not Knowing the Radius. Submitted. Appeared as discussion paper Nr. 81. SFB 373 (Quantification and Simulation of Economic Processes), Humboldt University, Berlin; also available under www.uni-bayreuth.de/departments/math/org/mathe7/RIEDER/pubs/RR.pdf
Kohl, M. (2005) Numerical Contributions to the Asymptotic Theory of Robustness. Bayreuth: Dissertation.
参见----------See Also----------
radiusMinimaxIC, leastFavorableRadius
radiusMinimaxIC，leastFavorableRadius
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 23:15:19
