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r - 在 Stargazer 中显示 Akaike 标准

转载 作者:行者123 更新时间:2023-12-04 19:28:59 26 4
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我有两个使用 lm 创建的线性模型我想与 stargazer 中的表进行比较包裹。在大多数情况下,我喜欢我得到的结果。但是 Akaike 信息标准没有显示。 The docs说我可以通过"aic"keep.stat包括它的论据。但它不存在。没有错误信息。

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

我看不出它不能包含它的任何原因。调用全局 AIC这些模型上的功能工作正常。
> AIC(model1)
[1] 1586.17
> AIC(model2)
[1] 1587.208

最佳答案

问题由 .AIC 给出stargazer:::.stargazer.wrap 内部定义的函数.
如您所见,此函数不计算 lm 的 AIC。楷模:

.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)
}
.get.model.name .AIC中的函数来电 .model.identify .如果组件 call型号为 lm() ,然后 .model.identify返回 ls :
if (object.name$call[1] == "lm()") { 
return("ls")
}

解决方案一 : 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
=================================

解决方案二 : 添加组件 AIC为对象建模。
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
======================================

关于r - 在 Stargazer 中显示 Akaike 标准,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/47494761/

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