plot.lts(robustbase)
plot.lts()所属R语言包：robustbase
Robust LTS Regression Diagnostic Plots
强大的LTS回归诊断图
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Four plots (selectable by which) are currently provided:
四图（由which选择）目前提供：
a plot of the standardized residuals versus their index,
指数的标准化残差与他们的图，
a plot of the standardized residuals versus fitted values,
图的标准化残差与拟合值，
a Normal Q-Q plot of the standardized residuals, and
正常的标准化残差的QQ图，
a regression diagnostic plot (standardized residuals versus robust distances of the predictor variables).
一个回归诊断图（标准化的残差与强大的预测变量的距离）。
用法----------Usage----------
## S3 method for class 'lts'
plot(x, which = c("all","rqq","rindex","rfit","rdiag"),
classic=FALSE, ask=(which=="all" && dev.interactive()), id.n, ...)
参数----------Arguments----------
参数：x
a lts object, typically result of ltsReg.
一个lts对象，通常的结果ltsReg。
参数：which
string indicating which plot to show. See the Details section for a description of the options. Defaults to "all". </table>
字符串，表示的曲线图显示。请参阅“详细资料”节中描述的选项。默认为"all"的。 </ TABLE>
参数：classic
whether to plot the classical distances too. Default is FALSE. </table>
是否绘制古典的距离。默认是FALSE。 </ TABLE>
参数：ask
logical indicating if the user should be asked before each plot, see par(ask=.). Defaults to which == "all" && dev.interactive().
逻辑表示，如果用户应该要求每个小区前，看到par(ask=.)。默认为which == "all" && dev.interactive()的。
参数：id.n
number of observations to be identified by a label starting with the most extreme. Default is the number of identified outliers (can be different for the different plots - see Details).
最极端的标签来识别观测到的数量。默认是确定离群值（可以是不同的不同图 - 见详情）。
参数：...
other parameters to be passed through to plotting functions.
其他参数被传递到绘图功能。
Details
详细信息----------Details----------
This function produces several plots based on the robust and classical regression estimates. Which of them to select is specified by the attribute which. The possible options are:
该函数产生几个图的鲁棒性和经典的回归估计的基础上。他们选择指定的属性which。可能的选项包括：
rqq: Normal Q-Q plot of the standardized residuals;
rqq：普通QQ的标准化残差图;
rindex: plot of the standardized residuals versus their
rindex：图与他们的标准化残差
rfit: plot of the standardized residuals versus fitted
rfit图的标准化残差与拟合
rdiag: regression diagnostic plot.
rdiag：回归诊断图。
The normal quantile plot produces a normal Q-Q plot of the standardized residuals. A line is drawn which passes through the first and third quantile. The id.n residuals with largest distances from this line are identified by labels (the observation number). The default for id.n is the number of regression outliers (lts.wt==0).
正常的位数图产生正常的标准化残差的QQ图。 A线绘制，其中通过在第一和第三分位数。 id.n这条线的最大距离残差识别标签（观察）。默认为id.n回归离群（lts.wt == 0）的数量。
In the Index plot and in the Fitted values plot the standardized residuals are displayed against the observation number or the fitted value respectively. A horizontal dashed line is drawn at 0 and two solid horizontal lines are located at +2.5 and -2.5. The id.n residuals with largest absolute values are identified by labels (the observation number). The default for id.n is the number regression outliers (lts.wt==0).
在指数图中的拟合值图的标准化残差观察数的拟合值分别上会显示。的水平虚线绘制在0和两个水平实线位于2.5和-2.5。该id.n残差与最大的绝对值的识别由标签（观察号码）。 id.n的默认值是回归离群（lts.wt == 0）。
The regression diagnostic plot, introduced by Rousseeuw and van Zomeren (1990), displays the standardized residuals versus robust distances. Following Rousseeuw and van Zomeren (1990), the horizontal dashed lines are located at +2.5 and -2.5 and the vertical line is located at the upper 0.975 percent point of the chi-squared distribution with p degrees of freedom. The id.n residuals with largest absolute values and/or largest robust Mahalanobis distances are identified by labels (the observation number). The default for id.n is the number of all outliers: regression outliers (lts.wt==0) + leverage (bad and good) points (RD > 0.975 percent point of the chi-squared distribution with p degrees of freedom).
推出的Rousseeuw和：面包车Zomeren（1990），回归诊断图，显示的标准化残差与强劲的距离。继Rousseeuw和van Zomeren（1990），位于2.5和-2.5的水平虚线，和垂直线的位于上部0.975个百分点带p自由度的卡方分布。绝对值最大的值和/或最大的健壮的马氏距离的id.n残差与识别由标签（观察号码）。默认情况下，为id.n所有离群的数量：回归异常值（lts.wt == 0）+杠杆（善恶）（RD> 0.975个百分点，与p自由度的卡方分布） 。
参考文献----------References----------
Unmasking Multivariate Outliers and Leverage Points. Journal of the American Statistical Association 85, 633–639.
A fast algorithm for the minimum covariance determinant estimator. Technometrics 41, 212–223.
参见----------See Also----------
covPlot
covPlot
实例----------Examples----------
data(hbk)
lts <- ltsReg(Y ~ ., data = hbk)
lts
plot(lts, which = "rqq")
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 22:12:40
