optIC(ROptEst)
optIC()所属R语言包：ROptEst
Generic function for the computation of optimally robust ICs
计算最佳强大的IC的通用功能
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Generic function for the computation of optimally robust ICs.
通用功能计算的最佳强大的IC。
用法----------Usage----------
optIC(model, risk, ...)
## S4 method for signature 'InfRobModel,asRisk'
optIC(model, risk, z.start = NULL, A.start = NULL,
upper = 1e4, lower = 1e-4,
OptOrIter = "iterate", maxiter = 50,
tol = .Machine$double.eps^0.4, warn = TRUE,
noLow = FALSE, verbose = NULL, ...)
## S4 method for signature 'InfRobModel,asUnOvShoot'
optIC(model, risk, upper = 1e4,
lower = 1e-4, maxiter = 50,
tol = .Machine$double.eps^0.4, warn = TRUE)
## S4 method for signature 'FixRobModel,fiUnOvShoot'
optIC(model, risk, sampleSize, upper = 1e4, lower = 1e-4,
maxiter = 50, tol = .Machine$double.eps^0.4,
warn = TRUE, Algo = "A", cont = "left",
verbose = NULL)
参数----------Arguments----------
参数：model
probability model.
概率模型。
参数：risk
object of class "RiskType".
对象类"RiskType"。
参数：...
additional parameters.
附加参数。
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
upper bound for the optimal clipping bound.
上界的最佳剪辑约束。
参数：lower
lower bound for the optimal clipping bound.
下界的最佳剪辑约束。
参数：maxiter
the maximum number of iterations.
最大迭代次数。
参数：tol
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：sampleSize
integer: sample size.
整数：样本量。
参数：Algo
"A" or "B".
“A”或“B”。
参数：cont
"left" or "right".
“左”或“右”。
参数：noLow
logical: is lower case to be computed?
符合逻辑的：是较低的情况下，要计算？
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：verbose
logical: if TRUE, some messages are printed
逻辑：如果TRUE，一些消息都印
Details
详细信息----------Details----------
In case of the finite-sample risk "fiUnOvShoot" one can choose between two algorithms for the computation of this risk where the least favorable contamination is assumed to be left or right of some bound. For more details
情况下的有限样本的风险"fiUnOvShoot"可以选择两种算法计算风险的最不利的污染被认为是左或右的一些结合。欲了解更多详情，
值----------Value----------
Some optimally robust IC is computed.
一些最佳鲁棒的IC被计算。
方法----------Methods----------
model = "InfRobModel", risk = "asRisk" computes optimally robust influence curve for robust models with infinitesimal neighborhoods and various asymptotic risks.
=的“InfRobModel”，风险模型=“asRisk”的计算最优强大的影响力曲线与无穷小的社区和各种渐近风险的可靠的模型。
model = "InfRobModel", risk = "asUnOvShoot" computes optimally robust influence curve for robust models with infinitesimal neighborhoods and asymptotic under-/overshoot risk.
=的“InfRobModel”，风险模型=“asUnOvShoot”的计算与无穷小的社区和可靠的模型渐近under-/overshoot风险的最佳强大的影响曲线。
model = "FixRobModel", risk = "fiUnOvShoot" computes optimally robust influence curve for robust models with fixed neighborhoods and finite-sample under-/overshoot risk.
=的“FixRobModel”，风险模型=“fiUnOvShoot”的计算最优强大的影响力曲线可靠的模型与固定的社区和有限样本under-/overshoot的风险。
（作者）----------Author(s)----------
Matthias Kohl <a href="mailto:Matthias.Kohl@stamats.de">Matthias.Kohl@stamats.de</a>
参考文献----------References----------
Huber, P.J. (1968) Robust Confidence Limits. Z. Wahrscheinlichkeitstheor. Verw. Geb. 10:269–278.
Kohl, M. (2005) Numerical Contributions to the Asymptotic Theory of Robustness. Bayreuth: Dissertation.
Kohl, M. and Ruckdeschel, P. (2010): R package distrMod: Object-Oriented Implementation of Probability Models. J. Statist. Softw. 35(10), 1–27
Kohl, M. and Ruckdeschel, P., and Rieder, H. (2010): Infinitesimally Robust Estimation in General Smoothly Parametrized Models. Stat. Methods Appl., 19, 333–354.
Rieder, H. (1980) Estimates derived from robust tests. Ann. Stats. 8: 106–115.
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.
Rieder, H., Kohl, M. and Ruckdeschel, P. (2001) The Costs of not Knowing the Radius. 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
参见----------See Also----------
InfluenceCurve-class, RiskType-class
InfluenceCurve-class，RiskType-class
实例----------Examples----------
B <- BinomFamily(size = 25, prob = 0.25)
## classical optimal IC[＃经典的最佳IC]
IC0 <- optIC(model = B, risk = asCov())
plot(IC0) # plot IC[图IC]
checkIC(IC0, B)
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注：
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发表于 2012-9-27 23:17:55
