plot.glm.diag(SMPracticals)
plot.glm.diag()所属R语言包：SMPracticals
Diagnostic plots for generalized linear models
广义线性模型的诊断图
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Makes plot of jackknife deviance residuals against linear predictor, normal scores plots of standardized deviance residuals, plot of approximate Cook statistics against leverage/(1-leverage), and case plot of Cook statistic.
使正常的分数标准偏差残差图，线性预测，对利用/（1 - 杠杆），Cook统计量的情况下，积图近似库克统计的折刀偏差残差对图。
用法----------Usage----------
## S3 method for class 'glm.diag'
plot(x, glmdiag = glm.diag(x), subset = NULL, iden = FALSE, labels = NULL, ret = FALSE, ...)
参数----------Arguments----------
参数：x
glm.object : the result of a call to glm()
glm.object的结果调用glm()
参数：glmdiag
Diagnostics of x obtained from a call to glm.diag. If it is not supplied then it is calculated.
诊断x调用glm.diag。如果它没有提供，则它被计算。
参数：subset
Subset of data for which glm fitting performed: should be the same as the subset option used in the call to glm() which generated x. Needed only if the subset= option was used in the call to glm.
子集dataGLM配件进行：应该是一样的subset使用在调用glm()产生x的选项。需要，如果subset=选项是用于调用glm。
参数：iden
A logical argument. If TRUE then, after the plots are drawn, the user will be prompted for an integer between 0 and 4. A positive integer will select a plot and invoke identify() on that plot. After exiting identify(), the user is again prompted, this loop continuing until the user responds to the prompt with 0. If iden is FALSE (default) the user cannot interact with the plots.
一个逻辑论据。如果TRUE那么，图绘制后，用户将被提示为0和4之间的整数。一个正整数，将选择一个图，并调用identify()了这一图。退出后identify()，的用户再次提示，这个循环继续，直到用户响应该提示，有0。如果idenFALSE（默认），用户可以不互动的图。
参数：labels
A vector of labels for use with identify() if iden is TRUE. If it is not supplied then the labels are derived from x.
的矢量标签用于identify()如果iden是TRUE。如果它没有提供，则标签来自x。
参数：ret
A logical argument indicating if glmdiag should be returned. The default is FALSE.
一个符合逻辑的论据，说明glmdiag如果应该返回。默认的FALSE。
参数：...
Other arguments, which are ignored. This is included only for compatibility with S3 methods.
其他参数，这些参数将被忽略。这是只包括与S3方法的兼容性。
Details
详细信息----------Details----------
The plot on the top left is a plot of the jackknife deviance residuals against the fitted values.
该图的左上角是一个图的拟合值的的折刀偏差残差对。
The plot on the top right is a normal QQ plot of the standardized deviance residuals. The dotted line is the expected line if the standardized residuals are normally distributed, i.e. it is the line with intercept 0 and slope 1.
该图的右上角是一个正常的标准偏差残差的QQ图。虚线是预期的标准化残差是正态分布的，也就是说，它是截距0，斜率为1的行。
The bottom two panels are plots of the Cook statistics. On the left is a plot of the Cook statistics against the standardized leverages. In general there will be two dotted lines on this plot. The horizontal line is at 8/(n-2p) where n is the number of observations and p is the number of parameters estimated. Points above this line may be points with high influence on the model. The vertical line is at 2p/(n-2p) and points to the right of this line have high leverage compared to the variance of the raw residual at that point. If all points are below the horizontal line or to the left of the vertical line then the line is not shown.
底部的两个面板库克统计图。在左边是一个图，库克统计，对标准化的利用。在一般会有此图上的两条虚线。水平线是8 /第（n-2p）中，其中n是观测值的个数，p是估计的参数的数目。这条线以上的点可能会在模型上具有高度影响力的点。的垂直线是2π/第（n-2p）的点，这条线的右侧有高杠杆相比，在该点的原始残差的方差。如果所有的点都低于水平线或垂直线的左侧的，则未示出的线。
The final plot again shows the Cook statistic this time plotted against case number enabling us to find which observations are influential.
最后的图再次显示了Cook统计暗算，使我们能够找到的意见有影响力的情况数量。
Use of iden=T is encouraged for proper exploration of these four plots as a guide to how well the model fits the data and whether certain observations have an unduly large effect on parameter estimates.
使用iden=T这四个图为指导，以鼓励适当的勘探模型拟合数据和参数估计中一些意见是否有过大的影响。
值----------Value----------
If ret is TRUE then the value of glmdiag is returned otherwise there is no returned value.
如果RET是TRUE然后glmdiag则返回，否则是没有返回值的。
（作者）----------Author(s)----------
Angelo Canty
参考文献----------References----------
<h3>See Also</h3>
实例----------Examples----------
require(MASS)
data(leuk, package="MASS")
leuk.mod <- glm(time~ag-1+log10(wbc),family=Gamma(log),data=leuk)
leuk.diag <- glm.diag(leuk.mod)
plot.glm.diag(leuk.mod,leuk.diag)
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 2012-9-30 10:41:17
