stargazer2 supports
fixest and alpaca model objects natively, with
two features that matter for modern applied work:
Fixed effects are reported as indicator rows (“Yes / No”) at the bottom of the table rather than as coefficient rows. This follows the convention in the trade and IO literatures where high-dimensional FEs are controls, not objects of interest.
Standard error types are auto-detected from the model object and reported in the table note. When different columns use different SE types, the note breaks them out by column group automatically.
stargazer2 accepts SEs through three mechanisms, applied
in order of precedence:
| Priority | Mechanism | When to use |
|---|---|---|
| 1 (highest) | vcov = list(V1, V2, ...) |
Any vcov matrix — most flexible |
| 2 | se = list(se1, se2, ...) |
Numeric SE vectors (drop-in for original stargazer scripts) |
| 3 (lowest) | Auto-extraction | fixest and alpaca models — SE type read from the model object |
Passing NULL for a specific entry in the
vcov list tells stargazer2 to fall back to
auto-extraction for that column. This is useful when mixing
lm models (with externally supplied vcov matrices) and
fixest models (which already carry their SE type).
library(wooldridge)
data(wage1)
wage1$region <- factor(
ifelse(wage1$northcen == 1, "northcen",
ifelse(wage1$south == 1, "south",
ifelse(wage1$west == 1, "west", "northeast"))),
levels = c("northeast", "northcen", "south", "west")
)
wage1$occupation <- factor(
ifelse(wage1$profocc == 1, "professional",
ifelse(wage1$clerocc == 1, "clerical",
ifelse(wage1$servocc == 1, "service", "other"))),
levels = c("other", "professional", "clerical", "service")
)
wage1$industry <- factor(
ifelse(wage1$construc == 1, "construction",
ifelse(wage1$ndurman == 1, "nondurable_manuf",
ifelse(wage1$trcommpu == 1, "transport",
ifelse(wage1$trade == 1, "trade",
ifelse(wage1$services == 1, "services",
ifelse(wage1$profserv == 1, "prof_services", "other")))))),
levels = c("other", "construction", "nondurable_manuf",
"transport", "trade", "services", "prof_services")
)We estimate five log-wage regressions with feols,
varying the set of fixed effects and using two-way clustering on region
× industry throughout. The interacted FE specification
region^industry absorbs a full set of region-by-industry
cells.
library(fixest)
f1 <- feols(lwage ~ educ + exper + tenure + female + married |
region, wage1, vcov = ~region^industry)
f2 <- feols(lwage ~ educ + exper + tenure + female + married |
occupation, wage1, vcov = ~region^industry)
f3 <- feols(lwage ~ educ + exper + tenure + female + married |
region + occupation, wage1, vcov = ~region^industry)
f4 <- feols(lwage ~ educ + exper + tenure + female + married |
region + occupation + industry,
wage1, vcov = ~region^industry)
f5 <- feols(lwage ~ educ + exper + tenure + female + married |
region^industry, wage1, vcov = ~region^industry)A bare call — no labels specified — already produces a complete
table. stargazer2 extracts the dependent variable name
(lwage) from the model formula and, since all five columns
share the same estimator, omits the redundant model-type row:
stargazer(f1, f2, f3, f4, f5, type = "text")
======================================================================
Dependent variable:
-------------------------------------------------
lwage
(1) (2) (3) (4) (5)
----------------------------------------------------------------------
educ 0.083*** 0.061*** 0.060*** 0.057*** 0.081***
(0.007) (0.007) (0.007) (0.008) (0.008)
exper 0.003* 0.002 0.002 0.002 0.004**
(0.002) (0.002) (0.002) (0.001) (0.002)
tenure 0.017*** 0.016*** 0.016*** 0.014*** 0.014***
(0.004) (0.004) (0.004) (0.003) (0.004)
female -0.291*** -0.256*** -0.263*** -0.264*** -0.285***
(0.039) (0.042) (0.043) (0.045) (0.039)
married 0.131*** 0.115*** 0.120*** 0.094** 0.097***
(0.034) (0.036) (0.037) (0.035) (0.033)
----------------------------------------------------------------------
Region FE Yes No Yes Yes No
Occupation FE No Yes Yes Yes No
Industry FE No No No Yes No
Region x Industry FE No No No No Yes
----------------------------------------------------------------------
Observations 526 526 526 526 526
R2 0.414 0.450 0.460 0.510 0.473
Adjusted R2 0.405 0.442 0.449 0.494 0.439
Within R2 0.407 0.282 0.287 0.267 0.378
Adjusted Within R2 0.401 0.275 0.280 0.260 0.371
======================================================================
Note: standard errors clustered by region-industry;
*p<0.1; **p<0.05; ***p<0.01 Labels can be overridden when needed. The LaTeX source below also renames the covariates for presentation:
stargazer(f1, f2, f3, f4, f5,
type = "latex",
title = "Log Wages: Varying Fixed Effects",
label = "tab:fe-wages",
dep.var.labels = "log(Wage)",
covariate.labels = c("Education", "Experience", "Tenure",
"Female", "Married"))
% Table produced by stargazer2 v.0.1.0 by Tom Zylkin, University of Richmond (tzylkin@richmond.edu)
% Original stargazer package by: Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.
% R package version 5.2.3. https://CRAN.R-project.org/package=stargazer
\begin{table}[!htbp] \centering
\caption{Log Wages: Varying Fixed Effects}
\label{tab:fe-wages}
\begin{tabular}{@{\extracolsep{5pt}}lccccc}
\hline
& \multicolumn{5}{c}{\textit{Dependent variable:}} \\
& \multicolumn{5}{c}{log(Wage)} \\
& (1) & (2) & (3) & (4) & (5)\\
\hline
Education & 0.083$^{***}$ & 0.061$^{***}$ & 0.060$^{***}$ & 0.057$^{***}$ & 0.081$^{***}$ \\
& (0.007) & (0.007) & (0.007) & (0.008) & (0.008) \\
Experience & 0.003$^{*}$ & 0.002 & 0.002 & 0.002 & 0.004$^{**}$ \\
& (0.002) & (0.002) & (0.002) & (0.001) & (0.002) \\
Tenure & 0.017$^{***}$ & 0.016$^{***}$ & 0.016$^{***}$ & 0.014$^{***}$ & 0.014$^{***}$ \\
& (0.004) & (0.004) & (0.004) & (0.003) & (0.004) \\
Female & $-$0.291$^{***}$ & $-$0.256$^{***}$ & $-$0.263$^{***}$ & $-$0.264$^{***}$ & $-$0.285$^{***}$ \\
& (0.039) & (0.042) & (0.043) & (0.045) & (0.039) \\
Married & 0.131$^{***}$ & 0.115$^{***}$ & 0.120$^{***}$ & 0.094$^{**}$ & 0.097$^{***}$ \\
& (0.034) & (0.036) & (0.037) & (0.035) & (0.033) \\
\hline
Region FE & Yes & No & Yes & Yes & No \\
Occupation FE & No & Yes & Yes & Yes & No \\
Industry FE & No & No & No & Yes & No \\
Region x Industry FE & No & No & No & No & Yes \\
\hline
Observations & 526 & 526 & 526 & 526 & 526 \\
R$^{2}$ & 0.414 & 0.450 & 0.460 & 0.510 & 0.473 \\
Adjusted R$^{2}$ & 0.405 & 0.442 & 0.449 & 0.494 & 0.439 \\
Within R$^{2}$ & 0.407 & 0.282 & 0.287 & 0.267 & 0.378 \\
Adjusted Within R$^{2}$ & 0.401 & 0.275 & 0.280 & 0.260 & 0.371 \\
\hline
\hline
\multicolumn{6}{p{\linewidth}}{\textit{Note:} standard errors clustered by region-industry; $^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01.} \\
\end{tabular}
\end{table} Two things to notice in both outputs:
region^industry is rendered as a single “Region x Industry
FE” row.The fixest package’s built-in trade dataset
provides a natural setting for showcasing different estimators. We
estimate a standard gravity equation for trade flows using OLS, Poisson
pseudo-maximum likelihood (PPML), and negative binomial — all with the
same four-way fixed effects.
data(trade, package = "fixest")
gravity_ols <- feols(log(Euros) ~ log(dist_km) |
Origin + Destination + Product + Year, trade)
gravity_pois <- fepois(Euros ~ log(dist_km) |
Origin + Destination + Product + Year, trade)
gravity_negbin <- fenegbin(Euros ~ log(dist_km) |
Origin + Destination + Product + Year, trade)Two further Poisson columns demonstrate the clustering-label
machinery: ~Origin^Destination clusters by the interaction
(one cluster per origin-destination pair);
~Origin+Destination is two-way clustering by origin and by
destination separately.
gravity_pois1 <- fepois(Euros ~ log(dist_km) |
Origin + Destination + Product + Year,
trade, vcov = ~Origin^Destination)
gravity_pois2 <- fepois(Euros ~ log(dist_km) |
Origin + Destination + Product + Year,
trade, vcov = ~Origin + Destination)With no arguments beyond the model list, stargazer2
automatically extracts the dependent variable names from each formula
(log(Euros) for the OLS model, Euros for the
count models), detects the estimator type for each column (OLS, Poisson,
Neg. Binomial), and reads the SE method from each model object:
stargazer(gravity_ols, gravity_pois, gravity_negbin,
gravity_pois1, gravity_pois2,
type = "text")
=========================================================================
Dependent variable:
------------------------------------------------------
log(Euros) Euros Euros Euros Euros
OLS Poisson Neg. Binomial Poisson Poisson
(1) (2) (3) (4) (5)
-------------------------------------------------------------------------
log(dist_km) -2.170*** -1.528*** -1.711*** -1.528*** -1.528***
(0.021) (0.000) (0.017) (0.077) (0.131)
-------------------------------------------------------------------------
Origin FE Yes Yes Yes Yes Yes
Destination FE Yes Yes Yes Yes Yes
Product FE Yes Yes Yes Yes Yes
Year FE Yes Yes Yes Yes Yes
-------------------------------------------------------------------------
Observations 38,325 38,325 38,325 38,325 38,325
R2 0.706 0.612 0.612 0.612
Adjusted R2 0.705
Within R2 0.219 0.307 0.016 0.307 0.307
Adjusted Within R2 0.219
Theta 0.549
=========================================================================
Note: (1) OLS standard errors; (2)-(3) MLE standard errors;
(4) standard errors clustered by Origin-Destination;
(5) standard errors clustered by Origin and
Destination; *p<0.1; **p<0.05; ***p<0.01 The same call in LaTeX produces submission-ready output:
stargazer(gravity_ols, gravity_pois, gravity_negbin,
gravity_pois1, gravity_pois2,
type = "latex",
title = "Gravity Equation for Trade Flows",
label = "tab:gravity")
% Table produced by stargazer2 v.0.1.0 by Tom Zylkin, University of Richmond (tzylkin@richmond.edu)
% Original stargazer package by: Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.
% R package version 5.2.3. https://CRAN.R-project.org/package=stargazer
\begin{table}[!htbp] \centering
\caption{Gravity Equation for Trade Flows}
\label{tab:gravity}
\begin{tabular}{@{\extracolsep{5pt}}lccccc}
\hline
& \multicolumn{5}{c}{\textit{Dependent variable:}} \\
& log(Euros) & Euros & Euros & Euros & Euros \\
& \textit{OLS} & \textit{Poisson} & \textit{Neg. Binomial} & \textit{Poisson} & \textit{Poisson} \\
& (1) & (2) & (3) & (4) & (5)\\
\hline
log(dist\_km) & $-$2.170$^{***}$ & $-$1.528$^{***}$ & $-$1.711$^{***}$ & $-$1.528$^{***}$ & $-$1.528$^{***}$ \\
& (0.021) & (0.000) & (0.017) & (0.077) & (0.131) \\
\hline
Origin FE & Yes & Yes & Yes & Yes & Yes \\
Destination FE & Yes & Yes & Yes & Yes & Yes \\
Product FE & Yes & Yes & Yes & Yes & Yes \\
Year FE & Yes & Yes & Yes & Yes & Yes \\
\hline
Observations & 38,325 & 38,325 & 38,325 & 38,325 & 38,325 \\
R$^{2}$ & 0.706 & 0.612 & & 0.612 & 0.612 \\
Adjusted R$^{2}$ & 0.705 & & & & \\
Within R$^{2}$ & 0.219 & 0.307 & 0.016 & 0.307 & 0.307 \\
Adjusted Within R$^{2}$ & 0.219 & & & & \\
Theta & & & 0.549 & & \\
\hline
\hline
\multicolumn{6}{p{\linewidth}}{\textit{Note:} (1) OLS standard errors; (2)-(3) MLE standard errors; (4) standard errors clustered by Origin-Destination; (5) standard errors clustered by Origin and Destination; $^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01.} \\
\end{tabular}
\end{table} The SE note shows per-column types: OLS standard errors for column (1), MLE standard errors for (2)–(3) (auto-detected from the fixest objects), standard errors clustered by Origin-Destination for column (4), and two-way clustered by Origin and Destination for column (5).
The alpaca package offers an alternative implementation
of fixed-effects GLMs (logit, probit, Poisson). stargazer2
supports alpaca::feglm objects through a companion pair of
vcov helpers:
alpaca_vcovSandwich() — heteroskedasticity-robust
(sandwich) SEsalpaca_vcovCL() — clustered SEs, with a formula
interface matching sandwich::vcovCLlibrary(alpaca)
# Logit model: P(married) as a function of wages and human capital,
# with occupation and industry fixed effects.
# industry must be in the FE specification for clustering by industry.
m_alp <- feglm(married ~ lwage + educ + exper | occupation + industry,
wage1, binomial("logit"))
V_robust <- alpaca_vcovSandwich(m_alp)
V_clustered <- alpaca_vcovCL(m_alp, cluster = ~industry)
stargazer(m_alp, m_alp,
type = "text",
dep.var.labels = "Married (0/1)",
covariate.labels = c("log(Wage)", "Education", "Experience"),
column.labels = c("Sandwich-robust", "Industry-clustered"),
vcov = list(V_robust, V_clustered))
=========================================
Dependent variable:
---------------------------
Married (0/1)
Sandwich-robust Industry-clustered
(1) (2)
-----------------------------------------
log(Wage) 0.890*** 0.890***
(0.263) (0.344)
Education 0.078 0.078***
(0.047) (0.026)
Experience 0.058*** 0.058***
(0.010) (0.007)
-----------------------------------------
Occupation FE Yes Yes
Industry FE Yes Yes
-----------------------------------------
Observations 526 526
R2 0.190 0.190
=========================================
Note: (1)
heteroskedasticity-robust
standard errors; (2)
standard errors clustered
by industry; *p<0.1;
**p<0.05; ***p<0.01 The SE note names the type for each column exactly as it does for fixest and sandwich models, confirming that the reporting machinery is consistent across all supported packages.