One summary table
Packages for reporting:
Summary Statistics Table:
Regression Table
library(jtools)
data(movies)
fit <- lm(metascore ~ budget + us_gross + year, data = movies)
summ(fit)
|
Observations
|
831 (10 missing obs. deleted)
|
|
Dependent variable
|
metascore
|
|
Type
|
OLS linear regression
|
|
F(3,827)
|
26.23
|
|
R²
|
0.09
|
|
Adj. R²
|
0.08
|
|
|
Est.
|
S.E.
|
t val.
|
p
|
|
(Intercept)
|
52.06
|
139.67
|
0.37
|
0.71
|
|
budget
|
-0.00
|
0.00
|
-5.89
|
0.00
|
|
us_gross
|
0.00
|
0.00
|
7.61
|
0.00
|
|
year
|
0.01
|
0.07
|
0.08
|
0.94
|
|
Standard errors: OLS
|
summ(
fit,
scale = TRUE,
vifs = TRUE,
part.corr = TRUE,
confint = TRUE,
pvals = FALSE
) # notice that scale here is TRUE
|
Observations
|
831 (10 missing obs. deleted)
|
|
Dependent variable
|
metascore
|
|
Type
|
OLS linear regression
|
|
F(3,827)
|
26.23
|
|
R²
|
0.09
|
|
Adj. R²
|
0.08
|
|
|
Est.
|
2.5%
|
97.5%
|
t val.
|
VIF
|
partial.r
|
part.r
|
|
(Intercept)
|
63.01
|
61.91
|
64.11
|
112.23
|
NA
|
NA
|
NA
|
|
budget
|
-3.78
|
-5.05
|
-2.52
|
-5.89
|
1.31
|
-0.20
|
-0.20
|
|
us_gross
|
5.28
|
3.92
|
6.64
|
7.61
|
1.52
|
0.26
|
0.25
|
|
year
|
0.05
|
-1.18
|
1.28
|
0.08
|
1.24
|
0.00
|
0.00
|
|
Standard errors: OLS; Continuous predictors are mean-centered and scaled by 1 s.d. The outcome variable remains in its original units.
|
#obtain clsuter-robust SE
data("PetersenCL", package = "sandwich")
fit2 <- lm(y ~ x, data = PetersenCL)
summ(fit2, robust = "HC3", cluster = "firm")
|
Observations
|
5000
|
|
Dependent variable
|
y
|
|
Type
|
OLS linear regression
|
|
F(1,4998)
|
1310.74
|
|
R²
|
0.21
|
|
Adj. R²
|
0.21
|
|
|
Est.
|
S.E.
|
t val.
|
p
|
|
(Intercept)
|
0.03
|
0.07
|
0.44
|
0.66
|
|
x
|
1.03
|
0.05
|
20.36
|
0.00
|
|
Standard errors: Cluster-robust, type = HC3
|
Model to Equation
# install.packages("equatiomatic") # not available for R 4.2
fit <- lm(metascore ~ budget + us_gross + year, data = movies)
# show the theoretical model
equatiomatic::extract_eq(fit)
# display the actual coefficients
equatiomatic::extract_eq(fit, use_coefs = TRUE)