R语言 ROptEstOld包 getRiskIC()函数中文帮助文档(中英文对照)

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getRiskIC(ROptEstOld)
getRiskIC()所属R语言包：ROptEstOld

                                        Generic function for the computation of a risk for an IC
                                         通用的风险计算功能的IC

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

Generic function for the computation of a risk for an IC.
通用的计算功能，为IC的风险。


用法----------Usage----------


getRiskIC(IC, risk, neighbor, L2Fam, ...)

## S4 method for signature 'IC,asCov,missing,missing'
getRiskIC(IC, risk, tol = .Machine$double.eps^0.25)

## S4 method for signature 'IC,asCov,missing,L2ParamFamily'
getRiskIC(IC, risk, L2Fam, tol = .Machine$double.eps^0.25)

## S4 method for signature 'IC,trAsCov,missing,missing'
getRiskIC(IC, risk, tol = .Machine$double.eps^0.25)

## S4 method for signature 'IC,trAsCov,missing,L2ParamFamily'
getRiskIC(IC, risk, L2Fam, tol = .Machine$double.eps^0.25)

## S4 method for signature 'IC,asBias,ContNeighborhood,missing'
getRiskIC(IC, risk, neighbor, tol = .Machine$double.eps^0.25)

## S4 method for signature 'IC,asBias,ContNeighborhood,L2ParamFamily'
getRiskIC(IC, risk, neighbor, L2Fam, tol = .Machine$double.eps^0.25)

## S4 method for signature 'IC,asBias,TotalVarNeighborhood,missing'
getRiskIC(IC, risk, neighbor, tol = .Machine$double.eps^0.25)

## S4 method for signature 'IC,asBias,TotalVarNeighborhood,L2ParamFamily'
getRiskIC(IC, risk, neighbor, L2Fam, tol = .Machine$double.eps^0.25)

## S4 method for signature 'IC,asMSE,UncondNeighborhood,missing'
getRiskIC(IC, risk, neighbor, tol = .Machine$double.eps^0.25)

## S4 method for signature 'IC,asMSE,UncondNeighborhood,L2ParamFamily'
getRiskIC(IC, risk, neighbor, L2Fam, tol = .Machine$double.eps^0.25)

## S4 method for signature 'TotalVarIC,asUnOvShoot,UncondNeighborhood,missing'
getRiskIC(IC, risk, neighbor)

## S4 method for signature 'IC,fiUnOvShoot,ContNeighborhood,missing'
getRiskIC(IC, risk, neighbor, sampleSize, Algo = "A", cont = "left")

## S4 method for signature 'IC,fiUnOvShoot,TotalVarNeighborhood,missing'
getRiskIC(IC, risk, neighbor, sampleSize, Algo = "A", cont = "left")



参数----------Arguments----------

参数：IC
object of class "InfluenceCurve"
对象的类"InfluenceCurve"


参数：risk
object of class "RiskType".
对象类"RiskType"。


参数：neighbor
object of class "Neighborhood".
对象类"Neighborhood"。


参数：L2Fam
object of class "L2ParamFamily".
对象类"L2ParamFamily"。


参数：...
additional parameters
额外的参数


参数：tol
the desired accuracy (convergence tolerance).  
所需的精度（收敛宽容）。


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”。


Details

详细信息----------Details----------

To make sure that the results are valid, it is recommended to include an additional check of the IC properties of IC
以确保结果是有效的，则建议包括一个额外的检查的集成电路性能的IC


值----------Value----------

The risk of an IC is computed.
计算的IC的风险。


方法----------Methods----------

  


IC = "IC", risk = "asCov", neighbor = "missing", L2Fam = "missing"  asymptotic covariance of IC.
IC =“IC”风险=“asCov”，邻居=“失踪”，L2Fam =“失踪”的渐近协方差IC。




IC = "IC", risk = "asCov", neighbor = "missing", L2Fam = "L2ParamFamily"  asymptotic covariance of IC under L2Fam.
IC =“IC”，风险=“asCov”邻居“失踪”，L2Fam =“L2ParamFamily的”渐近协方差IC下L2Fam。




IC = "IC", risk = "trAsCov", neighbor = "missing", L2Fam = "missing"  asymptotic covariance of IC.
IC =“IC”风险=“trAsCov”，邻居=“失踪”，L2Fam =“失踪”的渐近协方差IC。




IC = "IC", risk = "trAsCov", neighbor = "missing", L2Fam = "L2ParamFamily"  asymptotic covariance of IC under L2Fam.
IC =“IC”，风险=“trAsCov”邻居“失踪”，L2Fam =“L2ParamFamily的”渐近协方差IC下L2Fam。




IC = "IC", risk = "asBias", neighbor = "ContNeighborhood", L2Fam = "missing"  asymptotic bias of IC under convex contaminations.
IC =“IC”，风险的“asBias”，邻居=“ContNeighborhood”，L2Fam =“失踪”IC的凸污染下的渐近偏差。




IC = "IC", risk = "asBias", neighbor = "ContNeighborhood", L2Fam = "L2ParamFamily"  asymptotic bias of IC under convex contaminations and L2Fam.
IC =“IC”，的风险=“asBias”的，邻居的“ContNeighborhood”，L2Fam =“L2ParamFamily”渐近偏差的IC下凸污染和L2Fam。




IC = "IC", risk = "asBias", neighbor = "TotalVarNeighborhood", L2Fam = "missing"  asymptotic bias of IC in case of total variation neighborhoods.
IC =“IC”，风险的“asBias”，邻居=“TotalVarNeighborhood”，L2Fam =“失踪”的渐近偏差IC的总变化街区的情况下。




IC = "IC", risk = "asBias", neighbor = "TotalVarNeighborhood", L2Fam = "L2ParamFamily"  asymptotic bias of IC under L2Fam in case of total variation  neighborhoods.
IC =“IC”，风险的“asBias”，邻居的“TotalVarNeighborhood”，L2Fam =“L2ParamFamily”的渐近偏差IC下L2Fam的总变化街区的情况下。




IC = "IC", risk = "asMSE", neighbor = "UncondNeighborhood", L2Fam = "missing"  asymptotic mean square error of IC.
IC =“IC”，风险的“asMSE”，邻居的“UncondNeighborhood”，L2Fam =“失踪”渐近均方误差IC。




IC = "IC", risk = "asMSE", neighbor = "UncondNeighborhood", L2Fam = "L2ParamFamily"  asymptotic mean square error of IC under L2Fam.
IC =“IC”风险=“asMSE”的，邻居=“UncondNeighborhood”，L2Fam =“L2ParamFamily”渐近均方误差IC下L2Fam。




IC = "TotalVarIC", risk = "asUnOvShoot", neighbor = "UncondNeighborhood", L2Fam = "missing"  asymptotic under-/overshoot risk of IC.
IC =“TotalVarIC的”风险=的“asUnOvShoot”，邻居的“UncondNeighborhood”，L2Fam =“失踪”IC的渐近under-/overshoot风险。




IC = "IC", risk = "fiUnOvShoot", neighbor = "ContNeighborhood", L2Fam = "missing"  finite-sample under-/overshoot risk of IC.
IC =“IC”风险=“fiUnOvShoot”的，邻居=“ContNeighborhood”，L2Fam =“失踪”的有限样本under-/overshoot风险的IC。




IC = "IC", risk = "fiUnOvShoot", neighbor = "TotalVarNeighborhood", L2Fam = "missing"  finite-sample under-/overshoot risk of IC.   
IC =“IC”风险=“fiUnOvShoot”的，邻居=“TotalVarNeighborhood”，L2Fam =“失踪”的有限样本under-/overshoot风险的IC。


注意----------Note----------

This generic function is still under construction.
这个通用的功能尚在建设中。


（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&ndash;278.
Rieder, H. (1980) Estimates derived from robust tests. Ann. Stats. 8: 106&ndash;115.
Rieder, H. (1994) Robust Asymptotic Statistics. New York: Springer.
Kohl, M. (2005) Numerical Contributions to the Asymptotic Theory of Robustness.  Bayreuth: Dissertation.
Ruckdeschel, P. and Kohl, M. (2005) Computation of the Finite Sample Risk  of M-estimators on Neighborhoods.

参见----------See Also----------

getRiskIC-methods, InfRobModel-class
getRiskIC-methods，InfRobModel-class

转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。


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