使用寻星器在不同表列中的分解系数

Question

我的模型输出具有32个系数。我希望它们全部显示在一张表中，并提出了在一个栏中分别显示16个系数及其内在估计和p值的想法。有什么方法可以切片模型输出或告诉观星者将输出分成不同的列？

poisson_model <- 
  bind_rows(
    tibble(
      goals = database_mr$goals_team_home,
      team = database_mr$club_name_home,
      opponent=database_mr$club_name_away,
      home=1),
    tibble(
      goals=database_mr$goals_team_away,
      team=database_mr$club_name_away,
      opponent=database_mr$club_name_home,
      home=0)) %>%

  glm(goals ~ home + team +opponent, family=poisson(link=log),data=.)
summary(poisson_model)

Coefficients:
                              Estimate Std. Error z value Pr(>|z|)    
(Intercept)                    0.75216    0.22805   3.298 0.000973 ***
home                           0.24096    0.07588   3.176 0.001495 ** 
teamAdler Weseke II           -1.04748    0.24868  -4.212 2.53e-05 ***
teamBVH Dorsten               -0.28911    0.19946  -1.449 0.147200    
teamFC RW Dorsten             -0.87653    0.23168  -3.783 0.000155 ***
teamFenerbahce I. Marl        -0.56356    0.20580  -2.738 0.006175 ** 
teamSC Marl-Hamm              -0.14523    0.19169  -0.758 0.448688    
teamSC Reken II               -0.40481    0.20569  -1.968 0.049057 *  
teamSV Altendorf-Ulfkotte     -1.25184    0.27720  -4.516 6.30e-06 ***
teamSV Lembeck                -0.21607    0.19568  -1.104 0.269518    
teamSV Schermbeck II          -0.16674    0.18600  -0.896 0.370028    
teamTSV Raesfeld               0.02094    0.17866   0.117 0.906682    
teamTuS 05 Sinsen II          -0.90159    0.24070  -3.746 0.000180 ***
teamTuS Gahlen                -0.26630    0.19142  -1.391 0.164171    
teamTuS Velen                 -0.40946    0.20151  -2.032 0.042159 *  
teamVfL Ramsdorf               0.07215    0.17726   0.407 0.683973    
teamWestfalia Gemen II        -0.55929    0.20990  -2.665 0.007709 ** 
opponentAdler Weseke II        0.59518    0.21831   2.726 0.006405 ** 
opponentBVH Dorsten            0.05072    0.25027   0.203 0.839389    
opponentFC RW Dorsten          0.17760    0.23700   0.749 0.453647    
opponentFenerbahce I. Marl     0.10922    0.24428   0.447 0.654802    
opponentSC Marl-Hamm           0.50746    0.22592   2.246 0.024691 *  
opponentSC Reken II            0.69698    0.21994   3.169 0.001530 ** 
opponentSV Altendorf-Ulfkotte  1.08930    0.20466   5.322 1.02e-07 ***
opponentSV Lembeck             0.35564    0.22962   1.549 0.121428    
opponentSV Schermbeck II      -0.26666    0.27163  -0.982 0.326254    
opponentTSV Raesfeld          -0.08465    0.25771  -0.328 0.742563    
opponentTuS 05 Sinsen II       0.58102    0.21870   2.657 0.007891 ** 
opponentTuS Gahlen            -0.81158    0.31450  -2.581 0.009865 ** 
opponentTuS Velen              0.28034    0.23333   1.201 0.229578    
opponentVfL Ramsdorf          -0.43481    0.28270  -1.538 0.124030    
opponentWestfalia Gemen II     0.59072    0.22016   2.683 0.007293 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Answer 1

它不会很漂亮，但是可以工作...

Stargazer使您可以选择手动提供系数列表和相应的标准误差。您可以通过传递两个模型来“诱骗”观星者，然后手动传递系数。缺点是您需要运行“伪回归”，并需要调整obs，rsquared等。

这是使用主队和对战球队的二元指标进行回归并在两列中报告系数的最终结果：

代码：

library(stargazer)

# generate some data
d <- data.frame(score=rpois(1000,1),
                   home=sample(letters[1:10],1000,replace=T ),
                   opp=sample(letters[1:10],1000,replace=T ))
head(d)
       score home opp
1:     2    c   g
2:     1    j   g
3:     0    e   f
4:     1    f   j
5:     0    d   i
6:     1    d   f

# create a fake model
# note that home needs to include all of your factors
fake <- lm(score ~ home - 1, d)
# rename the coefficients
names(fake$coefficients) <- gsub("home","",names(fake$coefficients))

# run your regression 
m <- glm(score ~ home + opp - 1, d, family=poisson(link=log) )
summary(m)
m.s <- summary(m)

## write a function that fixes the names in the glm output
f <- function(x){
  names(x) <- gsub("home|opp","", names(x))
  return(x)
}

# now you can retrieve variables according to the prefix
m.s$coefficients[grepl("home", rownames(m.s$coefficients)), 1]
       homeb        homec        homed        homee        homef        homeg        homeh        homei        homej 
-0.008070675  0.287148469 -0.043331430  0.047798075  0.005438897  0.261008373  0.134444746  0.083937955  0.113310674 

stargazer(fake,fake,
          # coefficients
          coef = list(
            f( m.s$coefficients[grepl("home", rownames(m.s$coefficients)), 1]),
            f( m.s$coefficients[grepl("opp", rownames(m.s$coefficients)), 1])
          ),
          # standard errors
          se = list(
            f( m.s$coefficients[grepl("home", rownames(m.s$coefficients)), 2]),
            f( m.s$coefficients[grepl("opp", rownames(m.s$coefficients)), 2])
          ),
          column.labels = c("home", "opp"),
          # calculate pvalue using supplied coeff and se
          t.auto = T,
          out = "stargazer_d.html",
          omit.stat=c("all"),
          type = "html")