lmRob(robust)
lmRob()所属R语言包：robust
High Breakdown and High Efficiency Robust Linear Regression
的高击穿和高效率的鲁棒线性回归
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Performs a robust linear regression with high breakdown point and high efficiency regression.
执行具有高击穿点和高效率的回归稳健线性回归。
用法----------Usage----------
lmRob(formula, data, weights, subset, na.action,
model = TRUE, x = FALSE, y = FALSE, contrasts = NULL,
nrep = NULL, control = lmRob.control(...), ...)
参数----------Arguments----------
参数：formula
a formula object, with the response on the left of a ~ operator, and the terms, separated by + operators, on the right.
一个formula对象，分离+运营商，在右边的~运算符的左，响应的条款，。
参数：data
a data.frame in which to interpret the variables named in the formula, or in the subset and the weights argument. If this is missing, then the variables in the formula should be on the search list. This may also be a single number to handle some special cases - see below for details.
data.frame中解释的变量中的formula的名字命名，或在subset和weights参数。如果没有这个，那么formula中的变量应该上的搜索列表。这也可能是一个单一的数字处理一些特殊的情况下 - 见下文的详细信息。
参数：weights
vector of observation weights; if supplied, the algorithm fits to minimize the sum of a function of the square root of the weights multiplied into the residuals. The length of weights must be the same as the number of observations. The weights must be nonnegative and it is strongly recommended that they be strictly positive, since zero weights are ambiguous, compared to use of the subset argument.
观察权重向量，如果提供的话，该算法适合的权重乘以到残差的函数的平方根的总和最小化。 weights的长度作为观测值的数量必须是相同的。权重必须是非负的强烈建议，他们是严格正的，因为零的权重是不明确的，相比使用subset参数。
参数：subset
expression saying which subset of the rows of the data should be used in the fit. This can be a logical vector (which is replicated to have length equal to the number of observations), or a numeric vector indicating which observation numbers are to be included, or a character vector of the row names to be included. All observations are included by default.
表达说应在适合使用的哪个子集的行的数据。这可以是一个逻辑向量（这是复制的有长度等于观测值的数量），或一个数值向量表示观察号码将被包括，或者被包含的行的名称的字符矢量。默认情况下，所有的观测。
参数：na.action
a function to filter missing data. This is applied to the model.frame after any subset argument has been used. The default (with na.fail) is to create an error if any missing values are found. A possible alternative is na.exclude, which deletes observations that contain one or more missing values.
一个函数来筛选丢失的数据。这是适用于model.frame在任何subset参数已使用。默认值（用na.fail）是创建一个错误，如果发现任何遗漏值的。一个可能的选择是na.exclude，这将删除包含一个或多个缺失值的观察，。
参数：model
a logical flag: if TRUE, the model frame is returned in component model.
一个逻辑标志：如果TRUE，模型框架，则返回在组件model。
参数：x
a logical flag: if TRUE, the model matrix is returned in component x.
一个逻辑标志：如果TRUE，模型矩阵，则返回在组件x。
参数：y
a logical flag: if TRUE, the response is returned in component y.
一个逻辑标志：如果TRUE，响应返回在组件y。
参数：contrasts
a list giving contrasts for some or all of the factors appearing in the model formula. The elements of the list should have the same name as the variable and should be either a contrast matrix (specifically, any full-rank matrix with as many rows as there are levels in the factor), or else a function to compute such a matrix given the number of levels.
模型公式中出现的因素中的一些或所有的列表给反差。元素的列表中应具有相同的名称的变量，并应该是对比度基质（具体地，任何与尽可能多的行满秩矩阵因子有水平），否则一个函数来计算这样一个矩阵给定的级别数。
参数：nrep
the number of random subsamples to be drawn. If "Exhaustive" resampling is being used, the value of nrep is ignored.
随机子样本的数量进行绘制。正在使用"Exhaustive"如果重采样，nrep值将被忽略。
参数：control
a list of control parameters to be used in the numerical algorithms. See lmRob.control for the possible control parameters and their default settings.
要使用的控制参数的列表中的数值算法。 lmRob.control可能的控制参数及其默认设置。
参数：...
additional arguments are passed to the ccontrol functions.
额外的参数传递给陈婷婷，张代民，孙希华功能。
Details
详细信息----------Details----------
By default, the lmRob function automatically chooses an appropriate algorithm to compute a final robust estimate with high breakdown point and high efficiency. The final robust estimate is computed based on an initial estimate with high breakdown point. For the initial estimation, the alternate M-S estimate is used if there are any factor variables in the predictor matrix, and an S-estimate is used otherwise. To compute the S-estimate, a random resampling or a fast procedure is used unless the data set is small, in which case exhaustive resampling is employed. See lmRob.control for how to choose between the different algorithms.
默认情况下，lmRob功能，自动选择一个合适的算法来计算最终的具有高击穿点和高效率的稳健估计。最终的鲁棒估计计算的基础上具有高击穿点的初始估计。交替的MS的初始估计，估计用于在预测矩阵如果有任何因子变量，否则使用的S-估计。为了计算数据集的使用，除非是小，在这种情况下，详尽的重采样的S估计，一个随机的重采样或一个快速的过程。见lmRob.control如何选择不同的算法之间的。
值----------Value----------
a list describing the regression. Note that the solution returned here is an approximation to the true solution based upon a random algorithm (except when "Exhaustive" resampling is chosen). Hence you will get (slightly) different answers each time if you make the same call with a different seed. See lmRob.control for how to set the seed, and see lmRob.object for a complete description of the object returned.
列表中描述的回归。请注意，这里返回的解决方案是一个近似真实的解决方案，根据随机算法（除选择"Exhaustive"重采样）。因此，你会得到不同的答案（略），如果你每次做相同的呼叫使用不同的种子。见lmRob.control如何设置的种子，看到lmRob.object返回的对象的完整描述。
参考文献----------References----------
A class of robust and fully efficient regression estimates; mimeo, Universidad de Buenos Aires.
Algorithms, routines, and S functions for robust statistics. Wadsworth & Brooks/Cole, Pacific Grove, CA.
Robust regression with both continuous and categorical predictors. Journal of Statistical Planning and Inference 89, 197–214.
A Fast Procedure for Outlier Diagnostics in Large Regression Problems. Journal of the American Statistical Association 94, 434–445.
High breakdown-point and high efficiency estimates for regression. Annals of Statistics 15, 642–665.
A procedure for robust estimation and inference in linear regression; in Stahel, W. A. and Weisberg, S. W., Eds., Directions in robust statistics and diagnostics, Part II. Springer-Verlag.
参见----------See Also----------
lmRob.control, lmRob.object.
lmRob.control，lmRob.object。
实例----------Examples----------
data(stack.dat)
stack.rob <- lmRob(Loss ~ ., data = stack.dat)
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 22:35:16
