lmrob.M.S(robustbase)
lmrob.M.S()所属R语言包：robustbase
M-S regression estimators
M-S回归估计
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Computes an M-S-estimator for linear regression using the “M-S” algorithm.
使用“M-S”的算法进行线性回归计算的MS-估计。
用法----------Usage----------
lmrob.M.S(x, y, control, mf, split)
参数----------Arguments----------
参数：x
numeric matrix for the predictors
数字矩阵的预测
参数：y
numeric vector for the response
为响应的数值向量
参数：control
list as returned by lmrob.control
列表返回lmrob.control
参数：mf
model.frame as returned by model.frame
model.frame返回model.frame
参数：split
(optional) list as returned by splitFrame
（可选）列表返回splitFrame
Details
详细信息----------Details----------
This function is used by lmrob and not intended to be used on its own (because an M-S-estimator has too low efficiency "on its own").
此功能用于通过lmrob和不适合在其自身上使用（因为一个MS-估计有太低的效率“它自己的”）。
An M-S estimator is a combination of an S-estimator for the continuous variables and an L1-estimator for the categorical variables.
一个MS估算值是一个用于连续变量和分类变量的L1-估计器，用于组合的S-估计。
The S-estimator is estimated using a subsampling algorithm. If the model includes interactions between categorical (factor) and continuous variables, the subsampling algorithm might fail. In this case, one can choose to assign the interaction to the categorical side of variables rather than to the continuous side. This can be accomplished via the control argument split.type or by specifying split, see splitFrame.
S-估计估计使用二次抽样算法。如果模型包含绝对的“（factor）和连续变量之间的相互作用，欠采样算法可能会失败。在这种情况下，人们可以选择分类侧的变量，而不是连续侧分配的相互作用。这可以通过控制参数split.type或通过指定split，看到splitFrame。
Note that the return status converged does not refer to the actual convergence status. The algorithm used does not guarantee convergence and thus true convergence is almost never reached. This is, however, not a problem if the estimate is only used as initial estimate part of an MM or SMDM estimate.
需要注意的是返回的状态converged并不是指实际的收敛状态。使用的算法并不能保证收敛，因此几乎从来没有达到真正的融合。然而，这是不是一个问题，如果估计仅作为初步估计的MM或SMDM的估计。
The algorithm sometimes produces the warning message “Skipping design matrix equilibration (dgeequ): row ?? is exactly zero.”. This is just an artifact of the algorithm and can be ignored safely.
该算法有时会产生警告消息的“跳绳设计矩阵平衡（dgeequ）的行吗？完全是零。“这仅仅是一个工件的算法，并可以安全地被忽略。
值----------Value----------
A list with components
组件列表
参数：coefficients
numeric vector (length p) of M-S-regression coefficient estimates.
数字的向量（长p）MS-回归系数的估计。
参数：scale
the M-S-scale residual estimate
M-S-规模残余估计
参数：residuals
numeric vector (legnth n) of the residuals.
数字矢量（legnth n）的残差。
参数：weights
numeric vector (length n) of the robustness weights.
数字向量（长n）的鲁棒性权重。
参数：control
the same list as the control argument.
作为control参数列表。
参数：converged
Convergence status (always TRUE), needed for lmrob.fit.
收敛状态（总是TRUE），需要lmrob.fit。
（作者）----------Author(s)----------
Manuel Koller
参考文献----------References----------
Robust regression with both continuous and categorical predictors. Journal of Statistical Planning and Inference 89, 197–214.
参见----------See Also----------
lmrob; for a description of the available split types, see splitFrame.
lmrob;为可用的分割类型的描述，请参阅splitFrame。
lmRob in package robust uses a version of the M-S algorithm automatically when the formula contains factors. Our version however follows Maronna and Yohai (2000) more closely.
lmRob在包robust使用一个版本的MS算法自动时，公式中的因素。我们的版本但如下Maronna的和Yohai（2000）更紧密地。
实例----------Examples----------
data(education)
education <- within(education, Region <- factor(Region))
flm <- lm(Y ~ Region + X1 + X2 + X3, education)
x <- model.matrix(flm)
y <- education$Y # == model.response(model.frame(flm))[== model.response（model.frame（FLM））]
set.seed(17)
f.MS <- lmrob.M.S(x, y, control = lmrob.control(),
mf = model.frame(flm))
## The typical use of the "M-S" estimator -- as initial estimate :[＃典型的利用“MS”估计 - 初步估计：]
fmMS <- lmrob(Y ~ Region + X1 + X2 + X3, education,
init = "M-S")
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。
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发表于 2012-9-27 22:10:55
