lmrob.control(robustbase)
lmrob.control()所属R语言包：robustbase
Tuning parameters for lmrob
参数调整为lmrob的
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Tuning parameters for lmrob, the MM-type regression estimator and the associated S-, M- and D-estimators. Using setting="KS2011" sets the defaults as suggested by Koller and Stahel (2011).
调整参数lmrob，MM-型回归估计和相关的S-，M-和D-估计。使用setting="KS2011"设置的默认值，科勒和Stahel（2011）建议的。
用法----------Usage----------
lmrob.control(setting, seed = NULL, nResample = 500,
tuning.chi = NULL, bb = 0.5, tuning.psi = NULL,
max.it = 50, groups = 5, n.group = 400,
k.fast.s = 1, best.r.s = 2,
k.max = 200, maxit.scale = 200, k.m_s = 20,
refine.tol = 1e-7, rel.tol = 1e-7, solve.tol = 1e-7,
trace.lev = 0,
mts = 1000, subsampling = c("nonsingular", "simple"),
compute.rd = FALSE, method = 'MM',
psi = c('bisquare', 'lqq', 'welsh', 'optimal', 'hampel', 'ggw'),
numpoints = 10, cov = NULL,
split.type = c("f", "fi", "fii"), fast.s.large.n = 2000, ...)
参数----------Arguments----------
参数：setting
a string specifying alternative default values. Leave empty for the defaults or use "KS2011" for the defaults suggested by Koller and Stahel (2011). See Details.
一个字符串，指定替代默认值。留空为默认值，或者使用"KS2011"科勒和Stahel（2011）建议的默认值。查看详细信息。
参数：seed
an integer vector, the seed to be used for random re-sampling used in obtaining candidates for the initial S-estimator; see .Random.seed. The current value of .Random.seed will be preserved if seed is set; otherwise (by default), .Random.seed will be modified as usual from calls to runif().
一个整数向量，种子被用于随机重新采样中使用获得的初始的S-估计的候选;看到.Random.seed。的当前值.Random.seed保存，如果seed设置;否则（默认情况下），.Random.seed将像往常一样调用runif()修改。
参数：nResample
number of re-sampling candidates to be used to find the initial S-estimator. Currently defaults to 500 which works well in most situations (see references).
数重新采样被用来找到初始的S-估计的候选人。目前默认为500，效果很好，在大多数情况下（请参阅参考资料）。
参数：tuning.chi
tuning constant vector for the S-estimator. Sensible defaults are set according to psi to yield a 50% breakdown estimator. See Details.
时间常数向量的S-估计。合理的默认值设置根据psi产生50％的击穿估计。查看详细信息。
参数：bb
expected value under the normal model of the “chi” (rather rho) function with tuning constant equal to tuning.chi. This is used to compute the S-estimator.
正常模式下的“气”（而rho）函数的时间常数等于tuning.chi的预期值。这是用来计算的S-估计。
参数：tuning.psi
tuning constant vector for the redescending M-estimator. Depending on the value of psi this constant is set to yield an estimator with asymptotic efficiency of 95% for normal errors. See Details.
调整的redescending M-估计的常数向量。根据的价值psi此常数设置为95％的正常误差的渐近效率与产量的估计。查看详细信息。
参数：max.it
integer specifying the maximum number of IRWLS iterations.
整数，指定的最大数目IRWLS迭代。
参数：groups
(for the fast-S algorithm): Number of random subsets to use when the data set is large.
（快-S算法）：随机子集数时使用的数据集是大。
参数：n.group
(for the fast-S algorithm): Size of each of the groups above. Note that this must be at least p.
（快速-S算法）：groups上述的每一个的大小。请注意，这必须是至少的p。
参数：k.fast.s
(for the fast-S algorithm): Number of local improvement steps (“I-steps”) for each re-sampling candidate.
（快-S算法）：每个重采样候选人的改进措施（“I-步骤”）。
参数：k.m_s
(for the M-S algorithm): specifies after how many unsucessful refinement steps the algorithm stops.
的MS算法：算法结束后多少unsucessful细化步骤。
参数：best.r.s
(for the fast-S algorithm): Number of of best candidates to be iterated further (i.e., “refined”); is denoted t in Salibian-Barrera & Yohai(2006).
（快速算法）的最佳人选进一步进行迭代（即“精”）;表示t在Salibian - 巴雷拉和Yohai的（2006年）。
参数：k.max
(for the fast-S algorithm): maximal number of refinement steps for the “fully” iterated best candidates.
（快-S算法）：最大数量的细化步骤“完全”迭代最好的候选人。
参数：maxit.scale
integer specifying the maximum number of C level find_scale() iterations.
整数，指定C级find_scale()迭代的最大数量。
参数：refine.tol
(for the fast-S algorithm): relative convergence tolerance for the fully iterated best candidates.
（快-S算法）：相对收敛性完全重复的最佳候选人。
参数：rel.tol
(for the RWLS iterations of the MM algorithm): relative convergence tolerance for the parameter vector.
（的RWLS的MM算法的迭代）：相对收敛公差参数矢量。
参数：solve.tol
(for the S algorithm): relative tolerance for inversion. Hence, this corresponds to solve.default()'s tol.
（S算法）：相对宽容的反转。因此，这相当于solve.default()的tol。
参数：trace.lev
integer indicating if the progress of the MM-algorithm should be traced (increasingly); default trace.lev = 0 does no tracing.
整数，表示如果MM算法的进展要追溯到（越来越多）;默认trace.lev = 0并没有追踪。
参数：mts
maximum number of samples to try in subsampling algorithm.
最大数量的样本，试图在二次抽样算法。
参数：subsampling
type of subsampling to be used, simple for simple subsampling (default prior to version 0.9), nonsingular for nonsingular subsampling. See lmrob.S.
欠采样的使用，simple简单的二次抽样（默认版本0.9之前），nonsingular非奇异二次抽样的类型。见lmrob.S。
参数：compute.rd
logical indicating if robust distances (based on the MCD robust covariance estimator covMcd) are to be computed for the robust diagnostic plots. This may take some time to finish, particularly for large data sets, and can lead to singularity problems when there are factor explanatory variables (with many levels, or levels with “few” observations). Hence, is FALSE by default.
逻辑表明，如果强劲的距离（MCD鲁棒协方差估计的基础上covMcd）计算的强大的诊断图。这可能需要一些时间才能完成，尤其是对于大型数据集，并可能导致奇异性问题时有factor解释变量（与许多层面上，与“数”的意见或水平）。因此，默认情况下，FALSE。
参数：method
string specifying the estimator-chain. MM is interpreted as SM. See Details of lmrob for a description of the possible values.
字符串，该字符串指定的估计链。 MM被解释为SM。查看详情lmrob的描述可能的值。
参数：psi
string specifying the type ψ-function used. See Details of lmrob. Defaults to "bisquare" for S and MM-estimates, otherwise "lqq".
字符串，该字符串指定的类型ψ功能。请参阅详情lmrob。默认为"bisquare"S和MM估计，否则"lqq"。
参数：numpoints
number of points used in Gauss quadrature.
在高斯积分点的数量。
参数：cov
function or string with function name to be used to calculate covariance matrix estimate. The default is if(method %in% c('SM', 'MM')) ".vcov.avar1" else ".vcov.w". See Details of lmrob.
函数和函数名字符串被用来计算协方差矩阵估计。默认的if(method %in% c('SM', 'MM')) ".vcov.avar1" else ".vcov.w"。请参阅详情lmrob。
参数：split.type
determines how categorical and continuous variables are split. See splitFrame.
决定如何分类和连续变量的分裂。见splitFrame。
参数：fast.s.large.n
minimum number of observations required to switch from ordinary “fast S” algorithm to an efficient “large n” strategy.
最低数量的需要切换从普通的“快S”算法来高效的“大”战略的意见。
参数：...
further arguments to be added to control.
进一步的论据被添加到control。
Details
详细信息----------Details----------
The option setting="KS2011" alters the default arguments. They are changed to method = 'SMDM', psi = 'lqq', max.it = 500, k.max = 2000, cov = '.vcov.w'. The defaults of all the remaining arguments are not changed.
选项“setting="KS2011"改变了默认的参数。他们改变method = 'SMDM', psi = 'lqq', max.it = 500, k.max = 2000, cov = '.vcov.w'。所有其余的参数的默认值都没有改变。
By default, tuning.chi and tuning.psi are set to yield an MM-estimate with break-down point 0.5 and efficiency of 95\% at the normal. They are:
默认情况下，tuning.chi和tuning.psi设置为产生击穿点0.5和效率的95\%在正常的MM估计。它们分别是：
The values for the tuning constant for the ggw psi function are hard coded. The constants vector has four elements: minimal slope, b (controlling the bend at the maximum of the curve), efficiency, break-down point. Use NA for an unspecified value, see examples in the tables.
是硬编码的时间常数的值ggwPSI的功能。常数向量具有四个要素：最小的斜率，B（控制曲线的最大转弯处），效率高，击穿点。使用NA一个未确定的值，请参阅表中的例子。
The constants for the hampel psi function are chosen to have a redescending slope of -1/3. Constants for a slope of -1/2 would be
选择有一个redescending斜率hampel-1/3psi的函数的常数。常量的坡度-1/2
Alternative coefficients for an efficiency of 85\% at the normal are given in the table below.
替代效率系数85\%在正常的是在下面的表中给出。
（作者）----------Author(s)----------
Matias Salibian-Barrera, Martin Maechler and Manuel Koller
参考文献----------References----------
robust regression for small samples, Computational Statistics & Data Analysis 55(8), 2504–2515.
参见----------See Also----------
lmrob, also for references and examples.
lmrob，也引用和实例。
实例----------Examples----------
## Show the default settings:[＃显示的默认设置：]
str(lmrob.control())
## Use Koller & Stahel(2011)'s recommendation but a larger 'max.it':[＃科勒Stahel（2011年）的建议，但一个更大的“max.it：。]
str(ctrl <- lmrob.control("KS2011", max.it = 1000))
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注：
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发表于 2012-9-27 22:10:29
