anova.lmrob(robustbase)
anova.lmrob()所属R语言包：robustbase
Analysis of Robust Deviances ('anova') for "lmrob" Objects
的鲁棒Deviances分析（ANOVA）为“lmrob”对象
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Compute an analysis of robust Wald-type or deviance-type test tables for one or more linear regression models fitted by lmrob.
计算分析的强大的Wald-型或越轨行为的类型测试表的一个或多个线性回归模型拟合lmrob。
用法----------Usage----------
## S3 method for class 'lmrob'
anova(object, ..., test = c("Wald", "Deviance"))
参数----------Arguments----------
参数：object, ...
objects of class "lmrob", typically the result of a call to lmrob. ... arguments may also be symbolic descriptions of the reduced models (cf. argument formula in lm).
类的对象"lmrob"，通常调用lmrob的结果。 ...参数，也可能是象征性的描述，减少模型（参见参数formulalm）。
参数：test
a character string specifying the test statistic to be used. Can be one of "Wald" or "Deviance", with partial matching allowed, for specifying a "Wald"-type test or "Deviance"-type test.
一个字符的字符串指定的检验统计量被使用。可以是一个"Wald"或"Deviance"，部分匹配允许为指定的"Wald"型测试或"Deviance"型测试。
Details
详细信息----------Details----------
Specifying a single object gives a sequential analysis of a robust quasi-deviance table for that fit. That is, the reductions in the robust residual deviance as each term of the formula is added in turn are given in as the rows of a table. (Currently not yet implemented.)
指定一个单一的对象给出了一个强大的准偏差的表，适合序列分析。也就是说，作为下式的每个术语依次加入以减少鲁棒残余越轨为一个表的行中给出。 （目前尚未实现。）
If more than one object is specified, the table has a row for the residual quasi-degrees of freedom (however, this information is never used in the asymptotic tests). For all but the first model, the change in degrees of freedom and robust deviance is also given. (This only makes statistical sense if the models are nested.) As opposed to the convention, the models are forced to be listed from largest to smallest due to computational reasons.
如果指定了多个对象，表中有一排的残余准自由度（但是，这些信息是从来没有使用过中的渐近测试）。 ，但对于所有的第一款车型，变化程度的自由和强大的越轨行为。 （这只是统计意义上的如果模型嵌套的。反对“公约”），该机型是被迫上市，由于计算的原因，从最大到最小。
In addition, the table will contain test statistics and P values comparing the reduction in robust deviances for the model on the row to that on top of it. There are two different robust tests available: The "Wald"-type test (test = "Wald") and the Deviance-type test (test = "Deviance"). When using formula description of the nested models in the dot arguments and test = "Deviance", you may be urged to supply a lmrob fit for these models by an error message. This happens when the coefficients of the largest model reduced to the nested models result in invalid initial estimates for the nested models (indicated by robustness weights which are all 0).
此外，该表将包含检验统计量和P值减少在强大的deviances的模型上排在它上面的比较。有两种不同的可用的健壮的测试：“沃尔德”型试验（test = "Wald"）和越轨型试验（test = "Deviance"）。当使用公式描述的嵌套模型参数的点和test = "Deviance"，你可能被要求提供一个lmrob适合这些模型的错误讯息。发生这种情况时，最大的模型系数降低的嵌套模型结果的无效嵌套模型（鲁棒性的权重都为0）的初步估计。
The comparison between two or more models by anova.lmrob will only be valid if they are fitted to the same dataset.
比较两个或两个以上模型anova.lmrob才会有效，如果它们安装到相同的数据集。
值----------Value----------
Basically, an object of class anova inheriting from class data.frame.
基本上，类的一个对象anova继承类data.frame的。
（作者）----------Author(s)----------
Andreas Ruckstuhl
参见----------See Also----------
lmrob, anova.
lmrob，anova。
实例----------Examples----------
data(salinity)
summary(m0.sali <- lmrob(Y ~ . , data = salinity))
anova(m0.sali, Y ~ X1 + X3)
## -> X2 is not needed[＃ - > X2被无需]
(m1.sali <- lmrob(Y ~ X1 + X3, data = salinity))
anova(m0.sali, m1.sali) # the same as before[和以前一样]
anova(m0.sali, m1.sali, test = "Deviance")
## whereas 'X3' is highly significant:[＃而“X3”是非常重要的：]
m2 <- update(m0.sali, ~ . -X3)
anova(m0.sali, m2)
anova(m0.sali, m2, test = "Deviance")
if(require("MPV")) { ## Montgomery, Peck & Vining datasets[＃蒙哥马利，佩克与蔓生的数据集]
Jet <- table.b13
Jet.rflm1 <- lmrob(y ~ ., data=Jet,
control = lmrob.control(max.it = 500))
summary(Jet.rflm1)
anova(Jet.rflm1, y ~ x1 + x5 + x6, test="Wald")
try( anova(Jet.rflm1, y ~ x1 + x5 + x6, test="Deviance") )
## -> Error in anovaLm.... Please fit the nested models by lmrob[＃ - >在anovaLm的错误....请适合的嵌套模型lmrob]
## {{ since all robustness weights become 0 in the nested model ! }}[＃{{因为所有的鲁棒性权值变为0嵌套的模型！ }}]
## Ok: Do as the error message told us:[＃正确的错误消息告诉我们：]
## test by comparing the two *fitted* models:[＃测试通过比较两个装*模型：]
Jet.rflm2 <- lmrob(y ~ x1 + x5 + x6, data=Jet,
control=lmrob.control(max.it=100))
anova(Jet.rflm1, Jet.rflm2, test="Deviance")
} # end{"MPV" data}[{“MPV”数据}]
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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。
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发表于 2012-9-27 22:07:05
