Question

我正在使用stargazer来创建我的plm汇总表。

library(plm)
library(pglm)
data("Unions", package = "pglm")
anb1 <- plm(wage ~ union + exper + rural, Unions, model = "random", method = "bfgs")
stargazer(anb1)

不幸的是，stargazer不支持pglm模型。我正在寻找一个关于如何使用二进制因变量绘制pglm模型结果的解决方案，因为以下的观星者调用不适用于pglm模型。

anb2 <- pglm(union ~ wage + exper + rural, Unions, family = "binomial",
            model = "random", method = "bfgs")
stargazer(anb2)

除了提取每个摘要项目而不是分别格式化之外的任何替代方法？结果的类别是：

[1]＆＃34; maxLik＆＃34; ＆＃34;格言＆＃34; ＆＃34;列表＆＃34;

Answer 1

一种可能的解决方案是执行以下操作。

anb2 <- pglm(union ~ wage + exper + rural, Unions, family = "binomial",
         model = "random", method = "bfgs")
model = summary(anb2)

加载或安装以下库

 library(dplyr)
 library(xtable)
 library('gtools')

创建一个名为co-variates

的向量

var = c('Intercept', 'wage', 'exper', 'ruralyes', 'sigma')

然后做

 model_summary = model$estimate %>% as.data.frame() %>% 
 mutate(term = var, Estimate = round(Estimate, 2), SE = round(`Std. error`, 2), p.value = stars.pval(`Pr(> t)`)) %>% 
 select(term, Estimate, SE, p.value)

 > model_summary
        term Estimate   SE p.value
 1 Intercept    -2.86 0.23     ***
 2      wage     0.12 0.02     ***
 3     exper    -0.06 0.02       *
 4  ruralyes     0.09 0.25        
 5     sigma     4.30 0.23     ***

然后您可以在data.frame

上使用xtable

 library(xtable)
 xtable(model_summary)

Answer 2

另一种可能的（但并非完全令人满意的）解决方案。

library(plm)
library(pglm)
library(stargazer)
data("Unions", package = "pglm")

anb2 <- pglm(union ~ wage + exper + rural, Unions, family = "binomial",
            model = "random", method = "bfgs")

# A "fake" model
anb0 <- plm(union ~ wage + exper + rural, Unions, family = "binomial",
            model = "random", method = "bfgs")

tstats <- summary(anb2)$estimate[,3][-5]
pvs <- summary(anb2)$estimate[,4][-5]
SEs <- summary(anb2)$estimate[,2][-5]
coefs <- summary(anb2)$estimate[,1][-5]

stargazer(anb0, type="text", coef=list(coefs), se=list(SEs),
 p = list(pvs), omit.stat="all")

以下是stargazer：

生成的表格

====================================
             Dependent variable:    
         ---------------------------
                    union           
------------------------------------
wage              0.122***          
                   (0.024)          

exper             -0.058**          
                   (0.023)          

ruralyes            0.092           
                   (0.249)          

Constant          -2.857***         
                   (0.235)          

====================================
====================================
Note:    *p<0.1; **p<0.05; ***p<0.01

Answer 3

这是一个简单的提取函数，可以让texreg与pglm一起使用：

extract.pglm <- function (model, include.nobs = TRUE, include.loglik = TRUE, ...) {
   s <- summary(model, ...)
   coefficient.names <- rownames(s$estimate)
   coefficients <- s$estimate[, 1]
   standard.errors <- s$estimate[, 2]
   significance <- s$estimate[, 4]
   loglik.value <- s$loglik
   n <- nrow(model$model)
   gof <- numeric()
   gof.names <- character()
   gof.decimal <- logical()
   if (include.loglik == TRUE) {
      gof <- c(gof, loglik.value)
      gof.names <- c(gof.names, "Log-Likelihood")
      gof.decimal <- c(gof.decimal, TRUE)
   }
   if (include.nobs == TRUE) {
      gof <- c(gof, n)
      gof.names <- c(gof.names, "Num. obs.")
      gof.decimal <- c(gof.decimal, FALSE)
   }
   tr <- createTexreg(coef.names = coefficient.names, coef = coefficients, 
                 se = standard.errors, pvalues = significance, gof.names = gof.names, 
                 gof = gof, gof.decimal = gof.decimal)
   return(tr)
}

为了使此代码有效，您还应该注册该函数，以便在调用maxLik时默认处理pglm extract对象：

setMethod("extract", signature = className("maxLik", "maxLik"), 
      definition = extract.pglm)

之后，您可以将texreg与pglm一起使用，就像使用plm或texreg支持的其他模型一样。

Answer 4

Exam.find().or(subscriptions).then(docs => { /*logic here*/ }) .catch(error => { /*error logic here*/ })（1.36.24）的新版本在GitHub上可用（很快在CRAN上提供），并添加了pglm类。

Answer 5

这是一个简单得多的解决方案。截至2019年6月25日，Stargazer仍不支持pglm，但coeftest确实支持，只是通过coeftest将模型传递给stargazer。

（还要注意自@giamcomo以来pglm中数据对象名称的更改）

library(plm)
library(pglm)
library(lmtest)
library(stargazer)
data("UnionWage", package = "pglm")

anb2 <- pglm(union ~ wage + exper + rural, UnionWage, family = "binomial",
             model = "random", method = "bfgs")

stargazer(anb2)

summary(anb2)

stargazer(coeftest(anb2), type="text")

这是输出

> stargazer(anb2)

% Error: Unrecognized object type.
> 
> summary(anb2)
--------------------------------------------
Maximum Likelihood estimation
BFGS maximization, 35 iterations
Return code 0: successful convergence 
Log-Likelihood: -1655.034 
5  free parameters
Estimates:
            Estimate Std. error t value  Pr(> t)    
(Intercept) -3.43651    0.29175 -11.779  < 2e-16 ***
wage         0.82896    0.15014   5.521 3.37e-08 ***
exper       -0.06590    0.02318  -2.843  0.00447 ** 
ruralyes     0.07558    0.24866   0.304  0.76116    
sigma        4.26050    0.22606  18.847  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
--------------------------------------------
> 
> stargazer(coeftest(anb2), type="text")

====================================
             Dependent variable:    
         ---------------------------

------------------------------------
wage               0.83***          
                   (0.15)           
exper             -0.07***          
                   (0.02)           
ruralyes            0.08            
                   (0.25)           
sigma              4.26***          
                   (0.23)           
Constant          -3.44***          
                   (0.29)           
====================================
====================================
Note:    *p<0.1; **p<0.05; ***p<0.01
>

R stargazer与pglm模型 - 在plm模型中转换二项式pglm模型

5 个答案: