ltsReg(robustbase)
ltsReg()所属R语言包：robustbase
Least Trimmed Squares Robust (High Breakdown) Regression
最不修剪广场的乐百氏（高击穿）回归
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Carries out least trimmed squares (LTS) robust (high breakdown point) regression.
至少修剪法（LTS）鲁棒性（高击穿点）回归。
用法----------Usage----------
ltsReg(x, ...)
## S3 method for class 'formula'[类formula的方法]
ltsReg(formula, data, subset, weights, na.action,
model = TRUE, x.ret = FALSE, y.ret = FALSE,
contrasts = NULL, offset, ...)
## Default S3 method:[默认方法]
ltsReg(x, y, intercept = TRUE, alpha = 1/2, nsamp = 500,
adjust = FALSE, mcd = TRUE, qr.out = FALSE, yname = NULL,
seed = NULL, trace = FALSE, use.correction=TRUE, control, ...)
参数----------Arguments----------
参数：formula
a formula of the form y ~ x1 + x2 + ....
一个formula：的形式y ~ x1 + x2 + ...。
参数：data
data frame from which variables specified in formula are to be taken.
数据框formula是要采取指定的变量。
参数：subset
an optional vector specifying a subset of observations to be used in the fitting process.
一个可选的矢量指定的装配过程中可以使用的观测值的一个子集。
参数：weights
an optional vector of weights to be used in the fitting process. NOT USED YET.
在嵌合过程中要使用可选的权重向量。尚未使用。
参数：na.action
a function which indicates what should happen when the data contain NAs. The default is set by the na.action setting of options, and is na.fail if that is unset. The “factory-fresh” default is na.omit. Another possible value is NULL, no action. Value na.exclude can be useful.
一个函数，它表示当数据包含NA的，应该发生什么。默认设置是由na.action的options，是na.fail，如果是没有设置的。 “出厂时的默认是na.omit。另一种可能的值是NULL，没有行动。值na.exclude可能是有用的。
参数：model, x.ret, y.ret
logicals indicating if the model frame, the model matrix and the response are to be returned, respectively.
logicals表示，如果模型框架，模型矩阵和响应都是被退回，分别。
参数：contrasts
an optional list. See the contrasts.arg of model.matrix.default.
可选列表。请参阅contrasts.argmodel.matrix.default。
参数：offset
this can be used to specify an a priori known component to be included in the linear predictor during fitting. An offset term can be included in the formula instead or as well, and if both are specified their sum is used.
这可以被用来指定一个先验已知的组件被包括在配合期间的线性预测。式，而不是，或也可以包括在一个offset术语，和如果两者都指定使用它们的总和。
参数：x
a matrix or data frame containing the explanatory variables.
的矩阵或数据框中包含的解释变量。
参数：y
the response: a vector of length the number of rows of x. </table>
响应：ax的数量的行向量，长度。 </ TABLE>
参数：intercept
if true, a model with constant term will be estimated; otherwise no constant term will be included. Default is intercept = TRUE
如果情况属实，将常数项的模型估计;否则没有常数项将被纳入。默认是intercept = TRUE
参数：alpha
the percentage (roughly) of squared residuals whose sum will be minimized, by default 0.5. In general, alpha must between 0.5 and 1.
将被最小化的残差平方总和的百分比（大致），默认情况下，为0.5。在一般情况下，alpha必须在0.5和1之间。
参数：nsamp
number of subsets used for initial estimates or "best" or "exact". Default is nsamp = 500. For nsamp="best" exhaustive enumeration is done, as long as the number of trials does not exceed 5000. For "exact", exhaustive enumeration will be attempted however many samples are needed. In this case a warning message will be displayed saying that the computation can take a very long time.
用于初步估计或"best"或"exact"的子集数。默认是nsamp = 500。对于nsamp="best"穷举完成，只要试验的次数不超过5000。对于"exact"，穷举尝试，然而，许多样品需要。在这种情况下，将显示一条警告消息说，计算需要很长的时间。
参数：adjust
whether to perform intercept adjustment at each step. Since this can be time consuming, the default is adjust = FALSE.
是否执行拦截调整在每一步。因为这可能是耗费时间，默认是adjust = FALSE。
参数：mcd
whether to compute robust distances using Fast-MCD.
是否强大的计算使用快速MCD的距离。
参数：qr.out
whether to return the QR decomposition (see qr); defaults to false.
是否返回QR分解（见qr），默认为false。
参数：yname
the name of the dependent variable. Default is yname = NULL
因变量的名称。默认是yname = NULL
参数：seed
initial seed for random generator, see rrcov.control.
随机数发生器的初始种子，请参阅rrcov.control。
参数：trace
logical (or integer) indicating if intermediate results should be printed; defaults to FALSE; values >= 2 also produce print from the internal (Fortran) code.
逻辑（或整数）表示，如果中间结果应印，默认为FALSE值>= 2也产生打印的内部代码（Fortran语言）。
参数：use.correction
whether to use finite sample correction factors. Default is use.correction=TRUE
是否使用有限样本的校正因素。默认是use.correction=TRUE
参数：control
a list with estimation options - same as these provided in the function specification. If the control object is supplied, the parameters from it will be used. If parameters are passed also in the invocation statement, they will override the corresponding elements of the control object.
估计选项 - 这些在功能规格相同的列表。如果被供给的控制对象，从它的参数将被使用。如果参数传递的调用语句，它们将覆盖相应元素的控制对象。
参数：...
arguments passed to or from other methods.
传递的参数或其他方法。
Details
详细信息----------Details----------
The LTS regression method minimizes the sum of the h smallest squared residuals, where h > n/2, i.e. at least half the number of observations must be used. The default value of h (when alpha=1/2) is roughly n / 2, more precisely, (n+p+1) %/% 2 where n is the total number of observations, but by setting alpha, the user may choose higher values up to n, where h = h(α,n,p) = h.alpha.n(alpha,n,p). The LTS estimate of the error scale is given by the minimum of the objective function multiplied by a consistency factor and a finite sample correction factor – see Pison et al. (2002) for details. The rescaling factors for the raw and final estimates are returned also in the vectors raw.cnp2 and cnp2 of length 2 respectively. The finite sample corrections can be suppressed by setting use.correction=FALSE. The computations are performed using the Fast LTS algorithm proposed by Rousseeuw and Van Driessen (1999).
LTS回归方法的h最小的残差平方和最小的，h > n/2，也就是说，至少有一半的若干意见必须使用。 h（当alpha=1/2）大致n / 2，更确切地说，“(n+p+1) %/% 2其中n是总人数的观测，但通过设置的默认值alpha，用户可以选择更高的数值为n个，其中h = h(α,n,p) =h.alpha.n(alpha,n,p)。误差尺度的LTS估计由下式给出的目标函数的最小值乘以由一致性因子和一个有限的样品校正因子 - 见皮松等。 （2002年）的详细信息。重新定标因素的原料和最终的估计也raw.cnp2和cnp2长度为2的向量返回。有限样本的修正，可以抑制，通过设置use.correction=FALSE。使用快速LTS由Rousseeuw和Van Driessen的（1999）提出的算法，计算执行。
As always, the formula interface has an implied intercept term which can be removed either by y ~ x - 1 or y ~ 0 + x. See formula for more details.
像往常一样，配方接口有一个隐含的截距项可以删除y ~ x - 1或y ~ 0 + x的。见formula更多详情。
值----------Value----------
The function ltsReg returns an object of class "lts". The summary method function is used to obtain (and print) a summary table of the results, and plot() can be used to plot them, see the the specific help pages.
函数ltsReg返回一个对象类"lts"。 summary方法的功能是用来获取和打印结果的汇总表，plot()可用于绘制出来，看到具体的帮助页面。
The generic accessor functions coefficients, fitted.values and residuals extract various useful features of the value returned by ltsReg.
一般的访问功能coefficients，fitted.values和residuals提取各种有用的功能的ltsReg返回的值。
An object of class lts is a list containing at least the following components:
一个对象的类lts的是一个list，其中至少包含以下组件：
参数：crit
the value of the objective function of the LTS regression method, i.e., the sum of the h smallest squared raw residuals.
LTS回归的方法，即总和的h最小的原始残差平方的目标函数值的。
参数：coefficients
vector of coefficient estimates (including the intercept by default when intercept=TRUE), obtained after reweighting.
向量的系数估计值（包括默认情况下，intercept=TRUE），权重调整后得到的截距。
参数：best
the best subset found and used for computing the raw estimates, with length(best) == quan = h.alpha.n(alpha,n,p).
找到最好的子集，用于计算的原始估计，length(best) == quan = h.alpha.n(alpha,n,p)。
参数：fitted.values
vector like y containing the fitted values of the response after reweighting.
矢量像y的拟合值权重调整后的反应。
参数：residuals
vector like y containing the residuals from the weighted least squares regression.
像y加权最小二乘回归的残差向量。
参数：scale
scale estimate of the reweighted residuals.
重加权残差的规模估计。
参数：alpha
same as the input parameter alpha.
相同输入参数alpha。
参数：quan
the number h of observations which have determined the least trimmed squares estimator.
数h的意见，已经确定了至少修剪最小二乘估计。
参数：intercept
same as the input parameter intercept.
相同输入参数intercept。
参数：cnp2
a vector of length two containing the consistency correction factor and the finite sample correction factor of the final estimate of the error scale.
含有的一致性的校正因子和有限样品校正因子的最终估值的误差尺度的长度为2的向量。
参数：raw.coefficients
vector of raw coefficient estimates (including the intercept, when intercept=TRUE).
向量的原始系数估计值（包括拦截，当intercept=TRUE“）。
参数：raw.scale
scale estimate of the raw residuals.
规模估算的原始残留物。
参数：raw.resid
vector like y containing the raw residuals from the regression.
矢量像y包含原始残差的回归。
参数：raw.cnp2
a vector of length two containing the consistency correction factor and the finite sample correction factor of the raw estimate of the error scale.
含有的一致性校正因数和有限的样品校正因子的原始估计的误差尺度的长度为2的向量。
参数：lts.wt
vector like y containing weights that can be used in a weighted least squares. These weights are 1 for points with reasonably small residuals, and 0 for points with large residuals.
向量，如Y包含的权重，可以用来在加权最小二乘。这些权重是相当小的残差点，1和0点大残差。
参数：raw.weights
vector containing the raw weights based on the raw residuals and raw scale.
向量的原料的权重的基础上的原始的残差和原材料规模。
参数：method
character string naming the method (Least Trimmed Squares).
字符串命名的方法（最修剪平方）。
参数：X
the input data as a matrix (including intercept column if applicable).
输入数据矩阵（包括拦截列（如适用））。
参数：Y
the response variable as a vector.
响应变量的向量。
注意----------Note----------
We strongly recommend using lmrob() instead of ltsReg (See also below)!
我们强烈建议您使用lmrob()，而不是ltsReg（见下面）！
（作者）----------Author(s)----------
Valentin Todorov <a href="mailto:valentin.todorov@chello.at">valentin.todorov@chello.at</a>, based on
work written for S-plus by Peter Rousseeuw and Katrien van Driessen
from University of Antwerp.
参考文献----------References----------
Journal of the American Statistical Association 79, 871–881.
Robust Regression and Outlier Detection. Wiley.
A fast algorithm for the minimum covariance determinant estimator. Technometrics 41, 212–223.
Small Sample Corrections for LTS and MCD. Metrika 55, 111-123.
参见----------See Also----------
lmrob.S() provides a fast S estimator with similar breakdown point as ltsReg() but better efficiency.<br> For data analysis, rather use lmrob which is based on lmrob.S.
lmrob.S()提供一个快S估计类似故障点ltsReg()但更好的数据分析的效率。<BR>，而使用lmrob这是基于lmrob.S。
covMcd; summary.lts for summaries.
covMcd;summary.lts摘要。
The generic functions coef, residuals, fitted.
的通用功能coef，residuals，fitted。
实例----------Examples----------
data(heart)
## Default method works with 'x'-matrix and y-var:[＃默认的方法适用于X矩阵和Y-VAR：]
heart.x <- data.matrix(heart[, 1:2]) # the X-variables[X-变量]
heart.y <- heart[,"clength"]
ltsReg(heart.x, heart.y)
data(stackloss)
ltsReg(stack.loss ~ ., data = stackloss)
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 22:11:27
