使用 stargazer 拆分不同表格列中的系數

Question

我的 model output 具有 32 個系數。 我希望它們全部顯示在一張表中，並想出在一列中分別顯示 16 個系數及其固有估計值和 p 值的想法。 有沒有辦法將 model output 或告訴 stargazer 將 output 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

使用您的數據：

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")

有 3 列：

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 為您提供手動提供系數列表和相應標准誤差的選項。 您可以通過傳入兩個模型來“欺騙”觀星者，然后手動傳遞系數。 這很有效，因為 stargazer 按名稱匹配不同模型的系數。 缺點是您需要運行“假回歸”，並且需要調整 obs、rsquared 等。但是，您可以輕松提取所需信息並使用 add.lines 將其添加到表中。

這是對主隊和對方球隊使用二元指標進行回歸並在兩列中報告系數的最終結果：

代碼：

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")