kStepEstimator(RobAStBase)
kStepEstimator()所属R语言包：RobAStBase
Function for the computation of k-step estimates
k步估计函数的计算
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Function for the computation of k-step estimates.
k步估计的计算功能。
用法----------Usage----------
kStepEstimator(x, IC, start = NULL, steps = 1L,
useLast = getRobAStBaseOption("kStepUseLast"),
withUpdateInKer = getRobAStBaseOption("withUpdateInKer"),
IC.UpdateInKer = getRobAStBaseOption("IC.UpdateInKer"),
withICList = getRobAStBaseOption("withICList"),
withPICList = getRobAStBaseOption("withPICList"),
na.rm = TRUE, startArgList = NULL, ...)
参数----------Arguments----------
参数：x
sample
样品
参数：IC
object of class "IC"
对象的类"IC"
参数：start
initial estimate (for full parameter,i.e. in dimension k respective joint length of main and nuisance part of the parameter): either a numerical value, or an object of class "Estimate" or a function producing either a numerical value, or an object of class "Estimate" when evaluated at x,...; if missing or NULL, we use slot startPar of the L2family L2Fam from within IC
初步估计（为完整的参数，即在尺寸k各自的接头长度和滋扰主要参数的一部分）：任何一个数值，或一个类的对象"Estimate"或函数产生的数值值，或一个类的对象"Estimate"评价时，如果缺少或x,...，我们使用插槽NULL的L2family，startPar内<L2Fam; X>
参数：steps
integer: number of steps
整数步数
参数：useLast
which parameter estimate (initial estimate or k-step estimate) shall be used to fill the slots pIC, asvar and asbias of the return value.
参数估计（初步估计或k步估计）应被用于填补插槽pIC，asvar和asbias的返回值。
参数：withUpdateInKer
if there is a non-trivial trafo in the model with matrix D, shall the parameter be updated on ker(D)?
如果有一个不平凡的trafo模型中的矩阵D，应参数更新ker(D)？
参数：IC.UpdateInKer
if there is a non-trivial trafo in the model with matrix D, the IC to be used for this; if NULL the result of getboundedIC(L2Fam,D) is taken; this IC will then be projected onto ker(D).
如果有一个在模型中的非平凡trafo矩阵D，IC被用于此;如果NULL的结果getboundedIC(L2Fam,D)采取本IC然后将被投影到ker(D)。
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)。
参数：startArgList
a list of arguments to be given to argument start if the latter is a function; this list by default already starts with two unnamed items, the sample x, and the model eval(CallL2Fam(IC)).
的参数列表的说法start如果是后者的一个功能;，此列表默认情况下，已经开始有两个未命名的项目，样品x，和模型eval(CallL2Fam(IC))。
参数：withPICList
logical: shall slot pICList of return value be filled?
逻辑：应插槽pICList的返回值被填补？
参数：withICList
logical: shall slot ICList of return value be filled?
逻辑：应插槽ICList的返回值被填补？
参数：...
additional parameters
额外的参数
Details
详细信息----------Details----------
Given an initial estimation start, a sample x and an influence curve IC the corresponding k-step estimator is computed.
初步估计start，一个样品x的影响曲线IC相应的k步估计计算。
The default value of argument useLast is set by the global option kStepUseLast which by default is set to FALSE. In case of general models useLast remains unchanged during the computations. However, if slot CallL2Fam of IC generates an object of class "L2GroupParamFamily" the value of useLast is changed to TRUE. Explicitly setting useLast to TRUE should be done with care as in this situation the influence curve is re-computed using the value of the one-step estimate which may take quite a long time depending on the model.
参数useLast的默认值设置的全局选项“kStepUseLast默认情况下，它被设置成FALSE。在一般车型的情况下useLast的计算过程中保持不变。但是，如果插槽CallL2FamIC生成一个类的对象，"L2GroupParamFamily"useLast改变TRUE。明确设置useLastTRUE应小心，在这种情况下，影响曲线重新计算使用的一个估计值，这可能需要相当长的时间，视型号而定。
If useLast is set to TRUE and slot modifyIC of IC is filled with some function (which can be used to re-compute the IC for a different parameter), the computation of asvar, asbias and IC is based on the k-step estimate.
如果useLast被设置成TRUE和槽modifyICIC填充的一些功能（可以使用不同的参数重新计算的IC），计算asvar，asbias和ICk步估计的基础上。
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>,<br>
Peter Ruckdeschel <a href="mailto eter.Ruckdeschel@itwm.fraunhofer.de"> eter.Ruckdeschel@itwm.fraunhofer.de</a>
参考文献----------References----------
Rieder, H. (1994) Robust Asymptotic Statistics. New York: Springer.
Kohl, M. (2005) Numerical Contributions to the Asymptotic Theory of Robustness. Bayreuth: Dissertation.
参见----------See Also----------
IC-class, kStepEstimate-class
IC-class，kStepEstimate-class
实例----------Examples----------
if(require(ROptEst)){
## 1. generate a contaminated sample[＃1。产生污染的样品]
ind <- rbinom(100, size=1, prob=0.05)
x <- rnorm(100, mean=0, sd=(1-ind) + ind*9)
## 2. Kolmogorov(-Smirnov) minimum distance estimator[＃2。柯尔莫哥洛夫 - 斯米尔诺夫的最小距离估计]
(est0 <- MDEstimator(x=x, NormLocationScaleFamily()))
## 3. k-step estimation: radius known[＃3。 k步估计半径称为：]
N1 <- NormLocationScaleFamily(mean=estimate(est0)["mean"], sd=estimate(est0)["sd"])
N1.Rob <- InfRobModel(center = N1, neighbor = ContNeighborhood(radius = 0.5))
IC1 <- optIC(model = N1.Rob, risk = asMSE())
(est1 <- kStepEstimator(x, IC1, est0, steps = 3, withPIC = TRUE))
estimate(est1)
ksteps(est1)
pICList(est1)
start(est1)
## a transformed model[＃转换模型]
tfct <- function(x){
nms0 <- c("mean","sd")
nms <- "comb"
fval0 <- x[1]+2*x[2]
names(fval0) <- nms
mat0 <- matrix(c(1,2), nrow = 1, dimnames = list(nms,nms0))
return(list(fval = fval0, mat = mat0))
}
N1.traf <- N1; trafo(N1.traf) <- tfct
N1R.traf <- N1.Rob; trafo(N1R.traf) <- tfct
IC1.traf <- optIC(model = N1R.traf, risk = asMSE())
(est0.traf <- MDEstimator(x, N1.traf))
(est1.traf <- kStepEstimator(x, IC1.traf, est0, steps = 3,
withIC = TRUE, withPIC = TRUE, withUpdateInKer = FALSE))
(est1a.traf <- kStepEstimator(x, IC1.traf, est0, steps = 3,
withIC = TRUE, withPIC = TRUE, withUpdateInKer = TRUE))
estimate(est1.traf)
ksteps(est1.traf)
pICList(est1.traf)
startval(est1.traf)
untransformed.estimate(est1.traf)
uksteps(est1.traf)
ICList(est1.traf)
ustartval(est1.traf)
estimate(est1a.traf)
ksteps(est1a.traf)
pICList(est1a.traf)
startval(est1a.traf)
untransformed.estimate(est1a.traf)
uksteps(est1a.traf)
ICList(est1a.traf)
ustartval(est1a.traf)
}
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。
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发表于 2012-9-27 19:55:06
