Коэффициенты разделения в разных столбцах таблицы с помощью Stargazer

Question

Вывод моей модели имеет 32 коэффициента. Я хочу, чтобы все они отображались в одной таблице, и у меня возникла идея показать 16 коэффициентов в одном столбце, соответственно, с их собственными оценками и p-значениями. Есть ли способ нарезать вывод модели или сказать Stargazer разделить вывод на разные столбцы?

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

Используя ваши данные:

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")

С 3 столбцами:

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")

Answer 2

Stargazer дает вам возможность вручную предоставить список коэффициентов и соответствующих стандартных ошибок. Вы можете «обмануть» Stargazer с передачей в две модели, а затем вручную передать коэффициенты. Это хорошо работает, потому что Stargazer сопоставляет коэффициенты разных моделей по названию. Недостатком является то, что вам нужно запустить «поддельную регрессию» и настроить obs, rsquared et c. Однако вы можете легко извлечь необходимую информацию и добавить ее в таблицу с помощью add.lines.

Это конечный результат выполнения регрессии с двоичными показателями для домашней и противоположной команды и представления коэффициентов в двух столбцы:

Код:

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)
Call:
glm(formula = score ~ home + opp - 1, family = poisson(link = log), 
    data = d)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-1.68446  -1.36736  -0.00948   0.60121   2.85408  

Coefficients:
        Estimate Std. Error z value Pr(>|z|)  
homea  0.0286251  0.1407933   0.203   0.8389  
homeb -0.1563594  0.1352870  -1.156   0.2478  
homec -0.0673791  0.1378263  -0.489   0.6249  
homed -0.0425058  0.1383590  -0.307   0.7587  
homee -0.0612811  0.1463620  -0.419   0.6754  
homef -0.0028756  0.1407210  -0.020   0.9837  
homeg -0.0263096  0.1419598  -0.185   0.8530  
homeh -0.0421442  0.1371384  -0.307   0.7586  
homei  0.0871397  0.1382671   0.630   0.5285  
homej -0.0650161  0.1354183  -0.480   0.6311  
oppb  -0.0102711  0.1459574  -0.070   0.9439  
oppc   0.2625987  0.1426320   1.841   0.0656 .
oppd   0.1465768  0.1417666   1.034   0.3012  
oppe   0.0123358  0.1384327   0.089   0.9290  
oppf  -0.0007423  0.1381802  -0.005   0.9957  
oppg  -0.0035419  0.1481746  -0.024   0.9809  
opph   0.0852252  0.1378236   0.618   0.5363  
oppi  -0.0695733  0.1474909  -0.472   0.6371  
oppj  -0.0577961  0.1478874  -0.391   0.6959  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for poisson family taken to be 1)

    Null deviance: 1124.6  on 1000  degrees of freedom
Residual deviance: 1111.8  on  981  degrees of freedom
AIC: 2624.1

Number of Fisher Scoring iterations: 5
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")