在观星者中显示赤池标准

Question

我创建了两个线性模型lm，希望与stargazer包装中的表格进行比较。在大多数情况下，我喜欢得到的结果。但是Akaike信息准则并未显示。该文档说我可以传递"aic"的keep.stat参数包括它。但这不在那里。没有错误讯息。

stargazer(model1, model2, type="text", report="vc", header=FALSE,
          title="Linear Models Predicting Forest Land",
          keep.stat=c("aic", "rsq", "n"), omit.table.layout="n")

Linear Models Predicting Forest Land
==========================================
                      Dependent variable: 
                      --------------------
                             forest       
                         (1)        (2)   
------------------------------------------
log.MS.MIL.XPND.GD.ZS  -11.948    -12.557 

log.TX.VAL.AGRI.ZS.UN   2.310      2.299  

log.NY.GDP.MKTP.CD                 0.505  

Constant                40.857    28.365  

------------------------------------------
Observations             183        183   
R2                      0.142      0.146  
==========================================

我看不出它为什么不能包含它的任何原因。AIC在这些模型上调用全局函数可以正常工作。

> AIC(model1)
[1] 1586.17
> AIC(model2)
[1] 1587.208

Answer 1

问题是由.AIC内部定义的函数给出的stargazer:::.stargazer.wrap。
如您所见，此函数不计算lm模型的AIC ：

.AIC <- function(object.name) {
    model.name <- .get.model.name(object.name)
    if (model.name %in% c("coeftest")) {
        return(NA)
    }
    if (model.name %in% c("lmer", "lme", "nlme", "glmer", 
        "nlmer", "ergm", "gls", "Gls", "lagsarlm", "errorsarlm", 
        "", "Arima")) {
        return(as.vector(AIC(object.name)))
    }
    if (model.name %in% c("censReg")) {
        return(as.vector(AIC(object.name)[1]))
    }
    if (model.name %in% c("fGARCH")) {
        return(object.name@fit$ics["AIC"])
    }
    if (model.name %in% c("maBina")) {
        return(as.vector(object.name$w$aic))
    }
    if (model.name %in% c("arima")) {
        return(as.vector(object.name$aic))
    }
    else if (!is.null(.summary.object$aic)) {
        return(as.vector(.summary.object$aic))
    }
    else if (!is.null(object.name$AIC)) {
        return(as.vector(object.name$AIC))
    }
    return(NA)
}

call 中的.get.model.name函数。如果模型的组件是，则返回：.AIC.model.identifycalllm().model.identifyls

if (object.name$call[1] == "lm()") { 
   return("ls")
}

解决方案 1：使用add.lines.

set.seed(12345)
n <- 100
df <- data.frame(y=rnorm(n), x1=rnorm(n), x2=rnorm(n))

model1 <- lm(y ~ x1, data=df)
model2 <- lm(y ~ x2, data=df)

library(stargazer)
stargazer(model1, model2, type="text", report="vc", header=FALSE,
          title="Linear Models Predicting Forest Land",
          keep.stat=c("rsq", "n"), omit.table.layout="n",
          add.lines=list(c("AIC", round(AIC(model1),1), round(AIC(model2),1))))

输出是：

Linear Models Predicting Forest Land
=================================
             Dependent variable: 
             --------------------
                      y          
                (1)        (2)   
---------------------------------
x1             0.115             

x2                       -0.052  

Constant       0.240      0.243  

---------------------------------
AIC            309.4      310.3  
Observations    100        100   
R2             0.011      0.002  
=================================

解决方案 2：将组件添加AIC到模型对象。

model1 <- lm(y ~ x1, data=df)
model2 <- lm(y ~ x2, data=df)

model1$AIC <- AIC(model1)
model2$AIC <- AIC(model2)

stargazer(model1, model2, type="text", report="vc", header=FALSE,
          title="Linear Models Predicting Forest Land",
          keep.stat=c("aic", "rsq", "n"), omit.table.layout="n")

输出是

Linear Models Predicting Forest Land
======================================
                  Dependent variable: 
                  --------------------
                           y          
                     (1)        (2)   
--------------------------------------
x1                  0.115             

x2                            -0.052  

Constant            0.240      0.243  

--------------------------------------
Observations         100        100   
R2                  0.011      0.002  
Akaike Inf. Crit.  309.413    310.318 
======================================