deviance.cca(vegan)
deviance.cca()所属R语言包：vegan
Statistics Resembling Deviance and AIC for Constrained Ordination
统计外形酷似越轨行为的约束排序和AIC
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The functions extract statistics that resemble deviance and AIC from the result of constrained correspondence analysis cca or redundancy analysis rda. These functions are rarely needed directly, but they are called by step in automatic model building. Actually, cca and rda do not have AIC and these functions are certainly wrong.
功能提取类似的统计数据，偏差和AIC约束的对应分析cca或冗余分析rda的结果。这些函数很少需要直接，但他们被称为step的自动建模。事实上，cca和rda没有AIC和这些功能都肯定是错误的。
用法----------Usage----------
## S3 method for class 'cca'
deviance(object, ...)
## S3 method for class 'cca'
extractAIC(fit, scale = 0, k = 2, ...)
参数----------Arguments----------
参数：object
the result of a constrained ordination (cca or rda).
（cca或rda）的约束协调的结果。
参数：fit
fitted model from constrained ordination.
从约束协调的拟合模型。
参数：scale
optional numeric specifying the scale parameter of the model, see scale in step.
可选的数字指定的规模参数模型，scale中step。
参数：k
numeric specifying the "weight" of the equivalent degrees of freedom (=:edf) part in the AIC formula.
数字指定的“权重”相当程度的自由（=edf）的AIC公式中的一部分。
参数：...
further arguments.
进一步的论据。
Details
详细信息----------Details----------
The functions find statistics that resemble deviance and AIC in constrained ordination. Actually, constrained ordination methods do not have a log-Likelihood, which means that they cannot have AIC and deviance. Therefore you should not use these functions, and if you use them, you should not trust them. If you use these functions, it remains as your responsibility to check the adequacy of the result.
功能找到类似的统计数据，deviance和AIC的制约协调。其实，没有约束的协调方法对数似然，这意味着他们不能有AIC和偏差。因此，你不应该使用这些功能，如果你使用他们，你不应该相信他们。如果你使用这些功能，它仍然是你的责任，以检查是否有足够的结果。
The deviance of cca is equal to the Chi-square of the residual data matrix after fitting the constraints. The deviance of rda is defined as the residual sum of squares. The deviance function of rda is also used for capscale. Function extractAIC mimics extractAIC.lm in translating deviance to AIC.
cca的偏差等于卡方的剩余数据矩阵拟合后的约束。 rda的偏差被定义为残差平方和。越轨功能rda还用于capscale。功能extractAIC模仿extractAIC.lm的翻译偏差AIC的。
There is little need to call these functions directly. However, they are called implicitly in step function used in automatic selection of constraining variables. You should check the resulting model with some other criteria, because the statistics used here are unfounded. In particular, the penalty k is not properly defined, and the default k = 2 is not justified theoretically. If you have only continuous covariates, the step function will base the model building on magnitude of eigenvalues, and the value of k only influences the stopping point (but the variables with the highest eigenvalues are not necessarily the most significant in permutation tests in anova.cca). If you also have multi-class factors, the value of k will have a capricious effect in model building. The step function will pass arguments to add1.cca and drop1.cca, and setting test = "permutation" will provide permutation tests of each deletion and addition which can help in judging the validity of the model building.
这里有一点需要直接调用这些函数。然而，他们step使用的功能自动选择约束变量的隐式调用。你应该检查所产生的其他一些标准的模型，是毫无根据的，因为这里使用的统计信息。特别的罚款k是不正确的定义，并默认k = 2理论上是没有道理的。如果你只有step函数幅度的特征值，和值基础模型的建立k只影响到停止点（但最高的特征值的变量并不一定是连续协变量，最显着排列测试，anova.cca）。如果你也有多类因素，k的价值模型的建立将有一个反复无常的影响。 step函数参数传递给add1.cca和drop1.cca，并设置test = "permutation"将提供每个删除和添加的置换测试，它可以帮助判断的有效性，模型的建立。
值----------Value----------
The deviance functions return “deviance”, and extractAIC returns effective degrees of freedom and “AIC”.
deviance函数返回“越轨”，extractAIC返回的有效程度的自由，“AIC”。
注意----------Note----------
These functions are unfounded and untested and they should not be used directly or implicitly. Moreover, usual caveats in using step are very valid.
这些功能是毫无根据的和未经考验的，他们不应该直接或含蓄地使用。此外，通常使用的警告step是很有效的。
（作者）----------Author(s)----------
Jari Oksanen
参考文献----------References----------
Information-theoretic approach for selection of spatial and temporal models of community organization. Marine Ecology Progress Series <STRONG>253</STRONG>, 17–24.
参见----------See Also----------
cca, rda, anova.cca, step, extractAIC,
cca，rda，anova.cca，step，extractAIC，
实例----------Examples----------
# The deviance of correspondence analysis equals Chi-square[对应分析的偏差等于卡方]
data(dune)
data(dune.env)
chisq.test(dune)
deviance(cca(dune))
# Stepwise selection (forward from an empty model "dune ~ 1")[逐步选择（从空模型“沙丘~1”）]
ord <- cca(dune ~ ., dune.env)
step(cca(dune ~ 1, dune.env), scope = formula(ord))
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 2012-10-1 15:05:29
