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

loveR

getAsRisk(ROptEst)
getAsRisk()所属R语言包：ROptEst

                                        Generic Function for Computation of Asymptotic Risks
                                         通用函数计算的渐近风险

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

Generic function for the computation of asymptotic risks. This function is rarely called directly. It is used by  other functions.
通用函数计算的渐近风险。很少直接调用此函数。它被用来由其它函数。


用法----------Usage----------


getAsRisk(risk, L2deriv, neighbor, biastype, ...)

## S4 method for signature 'asMSE,UnivariateDistribution,Neighborhood,ANY'
getAsRisk(risk,
    L2deriv, neighbor, biastype, normtype = NULL, clip = NULL, cent = NULL, stand, trafo, ...)

## S4 method for signature 'asL1,UnivariateDistribution,Neighborhood,ANY'
getAsRisk(risk,
    L2deriv, neighbor, biastype, normtype = NULL, clip = NULL, cent = NULL, stand, trafo, ...)

## S4 method for signature 'asL4,UnivariateDistribution,Neighborhood,ANY'
getAsRisk(risk,
    L2deriv, neighbor, biastype, normtype = NULL, clip = NULL, cent = NULL, stand, trafo, ...)

## S4 method for signature 'asMSE,EuclRandVariable,Neighborhood,ANY'
getAsRisk(risk,
    L2deriv, neighbor, biastype, normtype = NULL, clip = NULL, cent = NULL, stand, trafo, ...)

## S4 method for signature 'asBias,UnivariateDistribution,ContNeighborhood,ANY'
getAsRisk(risk,
    L2deriv, neighbor, biastype, normtype = NULL, clip = NULL, cent = NULL, stand = NULL,trafo, ...)

## S4 method for signature 'asBias,UnivariateDistribution,ContNeighborhood,onesidedBias'
getAsRisk(
    risk, L2deriv, neighbor, biastype, normtype = NULL, clip = NULL, cent = NULL, stand = NULL, trafo, ...)

## S4 method for signature 'asBias,UnivariateDistribution,ContNeighborhood,asymmetricBias'
getAsRisk(
    risk, L2deriv, neighbor, biastype, normtype = NULL, clip = NULL, cent = NULL, stand = NULL, trafo, ...)

## S4 method for signature 'asBias,UnivariateDistribution,TotalVarNeighborhood,ANY'
getAsRisk(
    risk, L2deriv, neighbor, biastype, normtype = NULL, clip = NULL, cent = NULL, stand = NULL, trafo, ...)

## S4 method for signature 'asBias,RealRandVariable,ContNeighborhood,ANY'
getAsRisk(
    risk,L2deriv, neighbor, biastype, normtype = NULL, clip = NULL, cent = NULL, stand = NULL,
    Distr, DistrSymm, L2derivSymm,
    L2derivDistrSymm, Finfo, trafo, z.start, A.start, maxiter, tol,
    warn, verbose = NULL, ...)
## S4 method for signature 'asBias,RealRandVariable,TotalVarNeighborhood,ANY'
getAsRisk(
    risk, L2deriv, neighbor, biastype, normtype = NULL,
    clip = NULL, cent = NULL, stand = NULL, Distr, DistrSymm, L2derivSymm,
    L2derivDistrSymm, Finfo, trafo, z.start, A.start, maxiter, tol,
    warn, verbose = NULL, ...)

## S4 method for signature 'asCov,UnivariateDistribution,ContNeighborhood,ANY'
getAsRisk(
    risk, L2deriv, neighbor, biastype, normtype = NULL, clip, cent, stand, trafo = NULL, ...)

## S4 method for signature 'asCov,UnivariateDistribution,TotalVarNeighborhood,ANY'
getAsRisk(
    risk, L2deriv, neighbor, biastype, normtype = NULL, clip, cent, stand, trafo = NULL, ...)

## S4 method for signature 'asCov,RealRandVariable,ContNeighborhood,ANY'
getAsRisk(risk,
    L2deriv, neighbor, biastype, normtype = NULL, clip = NULL, cent, stand, Distr, trafo = NULL,
    V.comp =  matrix(TRUE, ncol = nrow(stand), nrow = nrow(stand)), w, ...)

## S4 method for signature 'trAsCov,UnivariateDistribution,UncondNeighborhood,ANY'
getAsRisk(
    risk, L2deriv, neighbor, biastype, normtype = NULL, clip, cent, stand, trafo = NULL, ...)

## S4 method for signature 'trAsCov,RealRandVariable,ContNeighborhood,ANY'
getAsRisk(risk,
    L2deriv, neighbor, biastype, normtype, clip, cent, stand, Distr, trafo = NULL,  
    V.comp =  matrix(TRUE, ncol = nrow(stand), nrow = nrow(stand)),
    w, ...)
  
## S4 method for signature 'asAnscombe,UnivariateDistribution,UncondNeighborhood,ANY'
getAsRisk(
    risk, L2deriv, neighbor, biastype, normtype = NULL, clip, cent, stand, trafo = NULL, FI, ...)

## S4 method for signature 'asAnscombe,RealRandVariable,ContNeighborhood,ANY'
getAsRisk(risk,
    L2deriv, neighbor, biastype, normtype, clip, cent, stand, Distr, trafo = NULL,
    V.comp =  matrix(TRUE, ncol = nrow(stand), nrow = nrow(stand)),
    FI, w, ...)
  

## S4 method for signature 'asUnOvShoot,UnivariateDistribution,UncondNeighborhood,ANY'
getAsRisk(
    risk, L2deriv, neighbor, biastype, normtype = NULL, clip, cent, stand, trafo, ...)

## S4 method for signature 'asSemivar,UnivariateDistribution,Neighborhood,onesidedBias'
getAsRisk(
    risk, L2deriv, neighbor, biastype, normtype = NULL, clip, cent, stand, trafo, ...)



参数----------Arguments----------

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


参数：L2deriv
L2-derivative of some L2-differentiable family of probability distributions.
L2-衍生的概率分布的一些L2-微的家庭。


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


参数：biastype
object of class "ANY".
对象类"ANY"。


参数：...
additional parameters; often used to enable flexible calls.
额外的参数;经常使用，以实现灵活的呼叫。


参数：clip
optimal clipping bound.
最佳剪裁的约束。


参数：cent
optimal centering constant.
最优的中心不变。


参数：stand
standardizing matrix.
规范矩阵。


参数：Finfo
matrix: the Fisher Information of the parameter.
矩阵的Fisher信息的参数。


参数：trafo
matrix: transformation of the parameter.
矩阵变换的参数。


参数：Distr
object of class "Distribution".
对象类"Distribution"。


参数：DistrSymm
object of class "DistributionSymmetry".
对象类"DistributionSymmetry"。


参数：L2derivSymm
object of class "FunSymmList".
对象类"FunSymmList"。


参数：L2derivDistrSymm
object of class "DistrSymmList".
对象类"DistrSymmList"。


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：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：normtype
object of class "NormType".
对象类"NormType"。


参数：V.comp
matrix: indication which components of the standardizing matrix have to be computed.
矩阵：指示哪些组件标准化矩阵以计算。


参数：w
object of class RobWeight; current weight
对象的类RobWeight;目前的体重


参数：FI
trace of the respective Fisher Information
跟踪各自的Fisher信息


参数：verbose
logical: if TRUE some diagnostics are printed out.
逻辑：如果TRUE一些诊断都印出来。


Details

详细信息----------Details----------

This function is rarely called directly. It is used by
很少直接调用此函数。它是利用


值----------Value----------

The asymptotic risk is computed.
的渐近计算风险。


方法----------Methods----------

  


risk = "asMSE", L2deriv = "UnivariateDistribution", neighbor = "Neighborhood", biastype = "ANY": computes asymptotic mean square error in methods for function getInfRobIC.
风险=“asMSE”，L2deriv =“邻居的UnivariateDistribution”，“邻居”，biastype =“ANY”：计算渐近均方误差函数getInfRobIC方法。




risk = "asL1", L2deriv = "UnivariateDistribution", neighbor = "Neighborhood", biastype = "ANY": computes asymptotic mean absolute error in methods for function getInfRobIC.
风险=“ASL1”，L2deriv =“邻居的UnivariateDistribution”，“邻居”，biastype =“ANY”：计算渐近平均绝对误差函数getInfRobIC方法。




risk = "asL4", L2deriv = "UnivariateDistribution", neighbor = "Neighborhood", biastype = "ANY": computes asymptotic mean power 4 error in methods for function getInfRobIC.
风险=的“asL4”，L2deriv =的“UnivariateDistribution”，邻居=“邻居”，biastype =“ANY”：，渐近平均功率错误的方法计算函数getInfRobIC。




risk = "asMSE", L2deriv = "EuclRandVariable", neighbor = "Neighborhood", biastype = "ANY": computes asymptotic mean square error in methods for function getInfRobIC.
风险=“asMSE”，L2deriv =“EuclRandVariable的”邻居“邻居”，biastype =“ANY”：计算函数getInfRobIC渐近均方误差的方法。




risk = "asBias", L2deriv = "UnivariateDistribution", neighbor = "ContNeighborhood", biastype = "ANY": computes standardized asymptotic bias in methods for function getInfRobIC.
风险=“asBias”的，L2deriv =的“UnivariateDistribution”，邻居=“ContNeighborhood”，biastype =“ANY”：在为函数getInfRobIC的方法计算标准化的渐近偏差。




risk = "asBias", L2deriv = "UnivariateDistribution", neighbor = "ContNeighborhood", biastype = "onesidedBias": computes standardized asymptotic bias in methods for function getInfRobIC.
风险=的“asBias”，L2deriv =的“UnivariateDistribution”，邻居=“ContNeighborhood”，biastype =的“onesidedBias”：在为函数getInfRobIC的方法计算标准化的渐近偏差。




risk = "asBias", L2deriv = "UnivariateDistribution", neighbor = "ContNeighborhood", biastype = "asymmetricBias": computes standardized asymptotic bias in methods for function getInfRobIC.
风险=的“asBias”，L2deriv =的“UnivariateDistribution”，邻居=“ContNeighborhood”，biastype =的“asymmetricBias”：在为函数getInfRobIC的方法计算标准化的渐近偏差。




risk = "asBias", L2deriv = "UnivariateDistribution", neighbor = "TotalVarNeighborhood", biastype = "ANY": computes standardized asymptotic bias in methods for function getInfRobIC.
风险=“asBias”的，L2deriv =的“UnivariateDistribution”，邻居=“TotalVarNeighborhood”，biastype =“ANY”：在为函数getInfRobIC的方法计算标准化的渐近偏差。




risk = "asBias", L2deriv = "RealRandVariable", neighbor = "ContNeighborhood", biastype = "ANY": computes standardized asymptotic bias in methods for function getInfRobIC.
风险=“asBias”，L2deriv =“RealRandVariable的”邻居=“ContNeighborhood”，biastype =“ANY”：在为函数getInfRobIC的方法计算标准化的渐近偏差。




risk = "asCov", L2deriv = "UnivariateDistribution", neighbor = "ContNeighborhood", biastype = "ANY": computes asymptotic covariance in methods for function getInfRobIC.
风险=的“asCov”，L2deriv =的“UnivariateDistribution”，邻居=“ContNeighborhood”，biastype =“ANY”：渐近协方差函数getInfRobIC方法计算。




risk = "asCov", L2deriv = "UnivariateDistribution", neighbor = "TotalVarNeighborhood", biastype = "ANY": computes asymptotic covariance in methods for function getInfRobIC.
风险=的“asCov”，L2deriv =的“UnivariateDistribution”，邻居=“TotalVarNeighborhood”，biastype =“ANY”：渐近协方差函数getInfRobIC方法计算。




risk = "asCov", L2deriv = "RealRandVariable", neighbor = "ContNeighborhood", biastype = "ANY": computes asymptotic covariance in methods for function getInfRobIC.
风险=“asCov”，L2deriv =“RealRandVariable的”邻居=“ContNeighborhood”，biastype =“ANY”：渐近协方差函数getInfRobIC方法计算。




risk = "trAsCov", L2deriv = "UnivariateDistribution", neighbor = "UncondNeighborhood", biastype = "ANY": computes trace of asymptotic covariance in methods  for function getInfRobIC.
风险=的“trAsCov”，L2deriv =的“UnivariateDistribution”，邻居=“UncondNeighborhood”，biastype =“ANY”：计算跟踪方法的渐近协方差函数getInfRobIC。




risk = "trAsCov", L2deriv = "RealRandVariable", neighbor = "ContNeighborhood", biastype = "ANY": computes trace of asymptotic covariance in methods for  function getInfRobIC.
风险=“trAsCov”，L2deriv =“RealRandVariable的”邻居=“ContNeighborhood”，biastype =“ANY”：计算跟踪方法的渐近协方差函数getInfRobIC。




risk = "asAnscombe", L2deriv = "UnivariateDistribution", neighbor = "UncondNeighborhood", biastype = "ANY": computes the ARE in the ideal model in methods  for function getInfRobIC.
风险=的“asAnscombe”，L2deriv =的“UnivariateDistribution”，邻居=“UncondNeighborhood”，biastype =“ANY”：计算的理想模型的方法函数getInfRobIC。




risk = "asAnscombe", L2deriv = "RealRandVariable", neighbor = "ContNeighborhood", biastype = "ANY": computes the ARE in the ideal model in methods for  function getInfRobIC.
风险=“asAnscombe”，L2deriv =“RealRandVariable的”邻居=“ContNeighborhood”，biastype =“ANY”：计算的理想模型的方法函数getInfRobIC。




risk = "asUnOvShoot", L2deriv = "UnivariateDistribution", neighbor = "UncondNeighborhood", biastype = "ANY": computes asymptotic under-/overshoot risk in methods for  function getInfRobIC.
危险=“asUnOvShoot”的，L2deriv =的“UnivariateDistribution”，邻居=“UncondNeighborhood”，biastype =“ANY”：函数getInfRobIC方法为，计算渐近under-/overshoot风险。




risk = "asSemivar", L2deriv = "UnivariateDistribution", neighbor = "Neighborhood", biastype = "onesidedBias": computes asymptotic semivariance in methods for function getInfRobIC.   
风险=的“asSemivar”，L2deriv =的“UnivariateDistribution”，邻居=“邻居”，biastype =“onesidedBias”的计算渐近半方差函数getInfRobIC方法。


（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. (1994) Robust Asymptotic Statistics. New York: Springer.
Ruckdeschel, P. and Rieder, H. (2004) Optimal Influence Curves for General Loss Functions. Statistics &amp; Decisions 22, 201-223.
Ruckdeschel, P. (2005) Optimally One-Sided Bounded Influence Curves. Mathematical Methods in Statistics 14(1), 105-131.
Kohl, M. (2005) Numerical Contributions to the Asymptotic Theory of Robustness.  Bayreuth: Dissertation.

参见----------See Also----------

asRisk-class
asRisk-class

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。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。