Show Akaike Criteria in Stargazer

Question

I have two linear models created with lm that I would like to compare with a table in the stargazer package. For the most part, I like the results I'm getting. But the Akaike Information Criterion is not showing. The docs say I can pass "aic" in the keep.stat argument to include it. But it's not there. No error messages.

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  
==========================================

I don't see any reason why it wouldn't be able to include it. Calling the global AIC function on these models works fine.

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

Answer 1

The problem is given by the .AIC function defined inside stargazer:::.stargazer.wrap.
As one can see, this function does not calculate AIC for lm models:

.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)
}

The .get.model.name function in .AIC calls .model.identify. If the component call of the model is lm(), then .model.identify returns ls:

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

Solution 1: Use 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))))

and the output is:

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  
=================================

Solution 2: Add the component AIC to model objects.

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")

and the output is

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 
======================================