How to report a standardized model in stargazer package? - r

Question

I made the following simple regression model and used stargazer to output a table that plots the standardized vs non-standardized regression model coefficients, standard errors and p-values.
library(lm.beta)
mod <- lm(mpg ~ cyl + disp, mtcars)
summary(mod)
mod_std <- lm.beta(mod)
summary(mod_std)$coe[, 2]
library(stargazer)
stargazer(mod, mod_std,
coef = list(mod$coefficients,
mod_std$standardized.coefficients),
type='text')
And this is the output:
==========================================================
Dependent variable:
----------------------------
mpg
(1) (2)
----------------------------------------------------------
cyl -1.587** -0.470
(0.712) (0.712)
disp -0.021** -0.423***
(0.010) (0.010)
Constant 34.661*** 0.000
(2.547) (2.547)
----------------------------------------------------------
Observations 32 32
R2 0.760 0.760
Adjusted R2 0.743 0.743
Residual Std. Error (df = 29) 3.055 3.055
F Statistic (df = 2; 29) 45.808*** 45.808***
==========================================================
Note: *p<0.1; **p<0.05; ***p<0.01
As can be observed here, the standard errors that are reported by the stargazer (for the coefficients) are the same for the standardized model and the non-standardized one. This is not correct as standard errors should change with the standardization of coefficients. Is there a way to report the correct standard errors? Or if not, simply remove them?
Lastly, what also changes from the standardized to the non-standardized models are the significance levels (of the coefficients). These should not change as they are not affected by standardization. Is there a way to prevent stargazer from modifying them? p or p.auto arguments maybe would work but I have no idea how to use them.
Reference for lm.beta: Stefan Behrendt (2014). lm.beta: Add Standardized Regression Coefficients to lm-Objects. R package version 1.5-1. https://CRAN.R-project.org/package=lm.beta

Answer 1

You would need to enter the additional values by hand, list-wise for each model, as you started with the coefficients. Standardized se=, the p= values (for the stars), ... as well as the the GOFs (R2, R2adj., ...), read options in help page: ?stargazer.
However, lm.beta appears to add nothing but the standardized coefficients, and none are yet calculated to report them.
Standardized standard errors are calculated using the formula SE*beta_star/beta.
So you could wrap a function, and calculate them, in order to fill them in the stargazer table:
std_se <- \(x) x[, 'Std. Error']*x[, 'Standardized']/x[, 'Estimate']
std_se(summary(mod_std)$coefficients)
# (Intercept) cyl disp
# 0.0000000 0.2109356 0.2109356
However, it might definitely be easier to calculate a actual standardized model
mod_std2 <- lm(mpg ~ cyl + disp, as.data.frame(scale(mtcars)))
summary(mod_std2) |> getElement('coefficients')
# Estimate Std. Error t value Pr(>|t|)
# (Intercept) 34.66099474 2.54700388 13.608536 4.022869e-14
# cyl -1.58727681 0.71184427 -2.229809 3.366495e-02
# disp -0.02058363 0.01025748 -2.006696 5.418572e-02
and put that in:
stargazer(mod, mod_std2, type='text')
# ==========================================================
# Dependent variable:
# ----------------------------
# mpg
# (1) (2)
# ----------------------------------------------------------
# cyl -1.587** -0.470**
# (0.712) (0.211)
# disp -0.021* -0.423*
# (0.010) (0.211)
# Constant 34.661*** -0.000
# (2.547) (0.090)
# ----------------------------------------------------------
# Observations 32 32
# R2 0.760 0.760
# Adjusted R2 0.743 0.743
# Residual Std. Error (df = 29) 3.055 0.507
# F Statistic (df = 2; 29) 45.808*** 45.808***
# ==========================================================
# Note: *p<0.1; **p<0.05; ***p<0.01

Answer 2

I managed to make the following script:
stargazer(mod_std,
coef=list(mod_std$standardized.coefficients),
se=list(summary(mod_std)$coe[, 2]),
p=list(summary(mod)$coe[, 4]),
type='text',
omit.stat = c("all"),
keep = c("cyl","disp"),
report = c('vcp'), notes.append = FALSE,
notes = "Coefficients are standardized")
With the following output:
===================================
Dependent variable:
-----------------------------
mpg
-----------------------------------
cyl -0.470
p = 0.034
disp -0.423
p = 0.055
===================================
===================================
Note: Coefficients are standardized
Here, the standardized coefficients are reported, together with the p-values from the original model (which should be unchanged across standardization).