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rglmstargazer

Split coefficients in different table columns with stargazer

My model output features 32 coefficients. I want them displayed all in one table and came up with the idea to show 16 coeffients in one column respectively with their inherent estimates and p-values. Is there any way to slice the model output or tell stargazer to split the output into different columns?

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
Solution

  • Using your data:

    enter image description here

    d <- readr::read_rds( "database_match_results_1920.rds") 
d <- 
  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))

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


# 
m <- glm(goals ~ home + team +opponent, family=poisson(link=log),data=d)
m.s <- summary(m)

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

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

    With 3 columns:

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