44.2 Model Comparison

fit <- lm(metascore ~ log(budget), data = movies)
fit_b <- lm(metascore ~ log(budget) + log(us_gross), data = movies)
fit_c <- lm(metascore ~ log(budget) + log(us_gross) + runtime, data = movies)
coef_names <- c("Budget" = "log(budget)", "US Gross" = "log(us_gross)",
                "Runtime (Hours)" = "runtime", "Constant" = "(Intercept)")
export_summs(fit, fit_b, fit_c, robust = "HC3", coefs = coef_names)
Table 38.1:
Model 1Model 2Model 3
Budget-2.43 ***-5.16 ***-6.70 ***
(0.44)   (0.62)   (0.67)   
US Gross       3.96 ***3.85 ***
       (0.51)   (0.48)   
Runtime (Hours)              14.29 ***
              (1.63)   
Constant105.29 ***81.84 ***83.35 ***
(7.65)   (8.66)   (8.82)   
N831       831       831       
R20.03    0.09    0.17    
Standard errors are heteroskedasticity robust. *** p < 0.001; ** p < 0.01; * p < 0.05.

Another package is modelsummary

library(modelsummary)
lm_mod <- lm(mpg ~ wt + hp + cyl, mtcars)
msummary(lm_mod, vcov = c("iid","robust","HC4"))
 (1)   (2)   (3)
(Intercept) 38.752 38.752 38.752
(1.787) (2.286) (2.177)
wt −3.167 −3.167 −3.167
(0.741) (0.833) (0.819)
hp −0.018 −0.018 −0.018
(0.012) (0.010) (0.013)
cyl −0.942 −0.942 −0.942
(0.551) (0.573) (0.572)
Num.Obs. 32 32 32
R2 0.843 0.843 0.843
R2 Adj. 0.826 0.826 0.826
AIC 155.5 155.5 155.5
BIC 162.8 162.8 162.8
Log.Lik. −72.738 −72.738 −72.738
F 50.171 31.065 32.623
RMSE 2.35 2.35 2.35
Std.Errors IID HC3 HC4 
modelplot(lm_mod, vcov = c("iid","robust","HC4"))

Another package is stargazer

library("stargazer")
stargazer(attitude)
#> 
#> % Table created by stargazer v.5.2.3 by Marek Hlavac, Social Policy Institute. E-mail: marek.hlavac at gmail.com
#> % Date and time: Fri, Jan 12, 2024 - 3:21:54 PM
#> \begin{table}[!htbp] \centering 
#>   \caption{} 
#>   \label{} 
#> \begin{tabular}{@{\extracolsep{5pt}}lccccc} 
#> \\[-1.8ex]\hline 
#> \hline \\[-1.8ex] 
#> Statistic & \multicolumn{1}{c}{N} & \multicolumn{1}{c}{Mean} & \multicolumn{1}{c}{St. Dev.} & \multicolumn{1}{c}{Min} & \multicolumn{1}{c}{Max} \\ 
#> \hline \\[-1.8ex] 
#> rating & 30 & 64.633 & 12.173 & 40 & 85 \\ 
#> complaints & 30 & 66.600 & 13.315 & 37 & 90 \\ 
#> privileges & 30 & 53.133 & 12.235 & 30 & 83 \\ 
#> learning & 30 & 56.367 & 11.737 & 34 & 75 \\ 
#> raises & 30 & 64.633 & 10.397 & 43 & 88 \\ 
#> critical & 30 & 74.767 & 9.895 & 49 & 92 \\ 
#> advance & 30 & 42.933 & 10.289 & 25 & 72 \\ 
#> \hline \\[-1.8ex] 
#> \end{tabular} 
#> \end{table}
## 2 OLS models
linear.1 <-
    lm(rating ~ complaints + privileges + learning + raises + critical,
       data = attitude)
linear.2 <-
    lm(rating ~ complaints + privileges + learning, data = attitude)
## create an indicator dependent variable, and run a probit model
attitude$high.rating <- (attitude$rating > 70)

probit.model <-
    glm(
        high.rating ~ learning + critical + advance,
        data = attitude,
        family = binomial(link = "probit")
    )
stargazer(linear.1,
          linear.2,
          probit.model,
          title = "Results",
          align = TRUE)
#> 
#> % Table created by stargazer v.5.2.3 by Marek Hlavac, Social Policy Institute. E-mail: marek.hlavac at gmail.com
#> % Date and time: Fri, Jan 12, 2024 - 3:21:54 PM
#> % Requires LaTeX packages: dcolumn 
#> \begin{table}[!htbp] \centering 
#>   \caption{Results} 
#>   \label{} 
#> \begin{tabular}{@{\extracolsep{5pt}}lD{.}{.}{-3} D{.}{.}{-3} D{.}{.}{-3} } 
#> \\[-1.8ex]\hline 
#> \hline \\[-1.8ex] 
#>  & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ 
#> \cline{2-4} 
#> \\[-1.8ex] & \multicolumn{2}{c}{rating} & \multicolumn{1}{c}{high.rating} \\ 
#> \\[-1.8ex] & \multicolumn{2}{c}{\textit{OLS}} & \multicolumn{1}{c}{\textit{probit}} \\ 
#> \\[-1.8ex] & \multicolumn{1}{c}{(1)} & \multicolumn{1}{c}{(2)} & \multicolumn{1}{c}{(3)}\\ 
#> \hline \\[-1.8ex] 
#>  complaints & 0.692^{***} & 0.682^{***} &  \\ 
#>   & (0.149) & (0.129) &  \\ 
#>   & & & \\ 
#>  privileges & -0.104 & -0.103 &  \\ 
#>   & (0.135) & (0.129) &  \\ 
#>   & & & \\ 
#>  learning & 0.249 & 0.238^{*} & 0.164^{***} \\ 
#>   & (0.160) & (0.139) & (0.053) \\ 
#>   & & & \\ 
#>  raises & -0.033 &  &  \\ 
#>   & (0.202) &  &  \\ 
#>   & & & \\ 
#>  critical & 0.015 &  & -0.001 \\ 
#>   & (0.147) &  & (0.044) \\ 
#>   & & & \\ 
#>  advance &  &  & -0.062 \\ 
#>   &  &  & (0.042) \\ 
#>   & & & \\ 
#>  Constant & 11.011 & 11.258 & -7.476^{**} \\ 
#>   & (11.704) & (7.318) & (3.570) \\ 
#>   & & & \\ 
#> \hline \\[-1.8ex] 
#> Observations & \multicolumn{1}{c}{30} & \multicolumn{1}{c}{30} & \multicolumn{1}{c}{30} \\ 
#> R$^{2}$ & \multicolumn{1}{c}{0.715} & \multicolumn{1}{c}{0.715} &  \\ 
#> Adjusted R$^{2}$ & \multicolumn{1}{c}{0.656} & \multicolumn{1}{c}{0.682} &  \\ 
#> Log Likelihood &  &  & \multicolumn{1}{c}{-9.087} \\ 
#> Akaike Inf. Crit. &  &  & \multicolumn{1}{c}{26.175} \\ 
#> Residual Std. Error & \multicolumn{1}{c}{7.139 (df = 24)} & \multicolumn{1}{c}{6.863 (df = 26)} &  \\ 
#> F Statistic & \multicolumn{1}{c}{12.063$^{***}$ (df = 5; 24)} & \multicolumn{1}{c}{21.743$^{***}$ (df = 3; 26)} &  \\ 
#> \hline 
#> \hline \\[-1.8ex] 
#> \textit{Note:}  & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
#> \end{tabular} 
#> \end{table}
# Latex
stargazer(
    linear.1,
    linear.2,
    probit.model,
    title = "Regression Results",
    align = TRUE,
    dep.var.labels = c("Overall Rating", "High Rating"),
    covariate.labels = c(
        "Handling of Complaints",
        "No Special Privileges",
        "Opportunity to Learn",
        "Performance-Based Raises",
        "Too Critical",
        "Advancement"
    ),
    omit.stat = c("LL", "ser", "f"),
    no.space = TRUE
)
# ASCII text output
stargazer(
    linear.1,
    linear.2,
    type = "text",
    title = "Regression Results",
    dep.var.labels = c("Overall Rating", "High Rating"),
    covariate.labels = c(
        "Handling of Complaints",
        "No Special Privileges",
        "Opportunity to Learn",
        "Performance-Based Raises",
        "Too Critical",
        "Advancement"
    ),
    omit.stat = c("LL", "ser", "f"),
    ci = TRUE,
    ci.level = 0.90,
    single.row = TRUE
)
#> 
#> Regression Results
#> ========================================================================
#>                                        Dependent variable:              
#>                          -----------------------------------------------
#>                                          Overall Rating                 
#>                                    (1)                     (2)          
#> ------------------------------------------------------------------------
#> Handling of Complaints   0.692*** (0.447, 0.937) 0.682*** (0.470, 0.894)
#> No Special Privileges    -0.104 (-0.325, 0.118)  -0.103 (-0.316, 0.109) 
#> Opportunity to Learn      0.249 (-0.013, 0.512)   0.238* (0.009, 0.467) 
#> Performance-Based Raises -0.033 (-0.366, 0.299)                         
#> Too Critical              0.015 (-0.227, 0.258)                         
#> Advancement              11.011 (-8.240, 30.262) 11.258 (-0.779, 23.296)
#> ------------------------------------------------------------------------
#> Observations                       30                      30           
#> R2                                0.715                   0.715         
#> Adjusted R2                       0.656                   0.682         
#> ========================================================================
#> Note:                                        *p<0.1; **p<0.05; ***p<0.01
stargazer(
    linear.1,
    linear.2,
    probit.model,
    title = "Regression Results",
    align = TRUE,
    dep.var.labels = c("Overall Rating", "High Rating"),
    covariate.labels = c(
        "Handling of Complaints",
        "No Special Privileges",
        "Opportunity to Learn",
        "Performance-Based Raises",
        "Too Critical",
        "Advancement"
    ),
    omit.stat = c("LL", "ser", "f"),
    no.space = TRUE
)

Correlation Table

correlation.matrix <-
    cor(attitude[, c("rating", "complaints", "privileges")])
stargazer(correlation.matrix, title = "Correlation Matrix")