R语言:AIC()函数中文帮助文档(中英文对照)

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AIC(stats)
AIC()所属R语言包：stats

                                        Akaike's An Information Criterion
                                         赤池是一个信息准则

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

Generic function calculating Akaike's information criterion for one or several fitted model objects for which a log-likelihood value can be obtained, according to the formula -2*log-likelihood + k*npar, where npar represents the number of parameters in the fitted model, and k = 2 for the usual AIC, or k = log(n) (n being the number of observations) for the so-called BIC or SBC (Schwarz's Bayesian criterion).
通用函数计算为一个或几个可以得到的对数似然值的拟合模型对象的赤池信息准则，根据公式-2*log-likelihood + k*npar，其中npar代表拟合模型中的参数数目，和k = 2通常AIC的，或k = log(n)（n的若干意见），为所谓的BIC或SBC（施瓦茨的贝叶斯准则）。


用法----------Usage----------


AIC(object, ..., k = 2)

BIC(object, ...)



参数----------Arguments----------

参数：object
a fitted model object for which there exists a logLik method to extract the corresponding log-likelihood, or an object inheriting from class logLik.
拟合模型对象，从而存在一个logLik方法来提取相应的日志的可能性，或继承类logLik对象。


参数：...
optionally more fitted model objects.
选择性更多拟合模型对象。


参数：k
numeric, the penalty per parameter to be used; the default k = 2 is the classical AIC.
数字，每个参数的罚款要使用的默认k = 2是古典的AIC的。


Details

详情----------Details----------

These are generic functions (with S4 generics defined in package stats4): however methods should be defined for the log-likelihood function logLik rather than these functions: the action of their default methods is to call logLik on all the supplied objects and assemble the results.
这些通用功能（在包中定义的中S4中泛型stats4）：但是方法应该被定义为对数似然函数，而不是这些功能logLik：他们的默认方法的作用是调用logLik所有的供应对象和组装的结果。

When comparing fitted objects, the smaller the AIC or BIC, the better the fit.
比较合身的对象时，较小的AIC或者BIC的，更好的契合。

The log-likelihood and hence the AIC/BIC is only defined up to an additive constant.  Different constants have conventionally be used for different purposes and so extractAIC and AIC may give different values (and do for models of class "lm": see the help for extractAIC).  Particular care is needed when comparing fits of different classes (with, for example, a comparison of a Poisson and gamma GLM being meaningless since one has a discrete response, the other continuous).
日志的可能性，从而为AIC / BIC的定义加法常数。不同的常数传统已被用于不同的目的，所以extractAIC和AIC可能会给不同的值（类模型做"lm"：看到extractAIC帮助）。比较适合不同类别，例如，一个泊松分布和伽玛的GLM比较是毫无意义的，因为人有离散的响应，其他连续时，需要特别注意。

BIC is defined as AIC(object, ..., k = log(nobs(object))). This needs the number of observations to be known: the default method looks first for a "nobs" attribute on the return value from the logLik method, then tries the nobs generic, and if neither succeed returns BIC as NA.
BIC被定义为AIC(object, ..., k = log(nobs(object)))。这需要若干意见，被称为：默认的方法寻找一个"nobs"属性，首先从logLik方法的返回值，然后尝试nobs通用的，如果既不成功返回的BICNA。


值----------Value----------

If just one object is provided, a numeric value with the corresponding AIC (or BIC, or ..., depending on k).
如果只是提供了一个对象，具有相应的工商行政管理机关的数值（或BIC的，或...，取决于k）。

If multiple objects are provided, a data.frame with rows corresponding to the objects and columns representing the number of parameters in the model (df) and the AIC or BIC.
如果多个对象提供一个data.frame与行相应的对象和列代表模型中的参数（df）和AIC或者BIC。


作者（S）----------Author(s)----------



Originally by Jos