I need to create publication-quality tables for my regression results. Help me: 1) Use stargazer

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ChatGPTOpenAI

I need to create publication-quality tables for my regression results. Help me: 1) Use stargazer

Certainly! The stargazer package in R is a great tool for creating publication-quality tables for regression results and other statistical outputs. Below is a step-by-step guide on how to use stargazer to create tables for regression results.

Step 1: Install and Load the `stargazer` Package

If you haven't already installed the stargazer package, you can do so by running the following command:

install.packages("stargazer")

Load the package in your R session:

library(stargazer)

Step 2: Fit Your Regression Model

Fit your regression model using either the lm() function for linear models or glm() for generalized linear models. Here’s an example of fitting a linear regression model:

# Example dataset
data(mtcars)# Fit a linear model
model1 <- lm(mpg ~ wt + hp, data = mtcars)
model2 <- lm(mpg ~ wt + hp + qsec, data = mtcars)

Step 3: Create a Table with `stargazer`

Use the stargazer function to create a summary table of your regression results. You can customize your output according to your preferences. Here’s a basic example:

# Create a table
stargazer(model1, model2,
          type = "text",            # Use 'text' for console output; change to 'html' or 'latex' for file output
          title = "Regression Results",
          align = TRUE,
          dep.var.labels = "Miles per Gallon (mpg)",
          covariate.labels = c("Weight (wt)", "Horsepower (hp)", "Quarter Mile Time (qsec)"),
          out = "regression_results.txt")  # Optional: specify file to save the results

Step 4: Customize Your Table

You can customize your output table in various ways:

add.lines: Add additional lines with custom text.

no.space: Remove extra space between lines.

digits: Specify the number of decimal places to display.

star.char: Customize the significance stars for p-values.

Example of a more customized table:

stargazer(model1, model2,
          type = "text", 
          title = "Regression Results",
          dep.var.labels.include = FALSE,
          column.labels = c("Model 1", "Model 2"),
          covariate.labels = c("Weight (wt)", "Horsepower (hp)", "Quarter Mile Time (qsec)"),
          stars = TRUE,
          star.char = c("*", "**", "***"),
          star.cutoffs = c(0.1, 0.05, 0.01),
          align = TRUE,
          out = "regression_results.txt")

Step 5: Output Options

The type argument in stargazer allows you to choose different output formats:

type = "text" to output directly in the R console.

type = "html" to create an HTML table.

type = "latex" to create a LaTeX table (ideal for academic papers).

Sample Output

Here is an example of what the output in your console might look like when you run stargazer:

=============================================
                  Dependent variable:        
              -------------------------------
                           mpg                
                            Model 1   Model 2 
---------------------------------------------
wt                          -3.800*** -3.456*** 
                            (0.633)    (0.642)   
hp                          -0.031     -0.034*   
                            (0.013)    (0.014)   
qsec                                    1.225*** 
                                        (0.318)   
Constant                     37.285***  37.227*** 
                            (1.598)    (1.262)   
---------------------------------------------
Observations                  32         32       
R2                           0.826      0.848     
Adjusted R2                  0.819      0.832     
=============================================
* p<0.1; ** p<0.05; *** p<0.01

This gives you a comprehensive table that includes estimates, standard errors, and significance levels for your regression models.

Conclusion

Using stargazer is straightforward and allows you to create high-quality tables for your regression results easily. Experiment with different settings until you achieve the desired presentation for your publication. If you need more customization or have specific requirements, feel free to ask!

{DateTime.UtcNow:HH:mm}

GeminiGoogle

I need to create publication-quality tables for my regression results. Help me: 1) Use stargazer

stargazer is an incredibly powerful and flexible R package for creating beautiful, publication-quality regression tables. It can output tables in LaTeX, HTML, or plain text, making it suitable for a wide range of needs.

Here's a comprehensive guide to using stargazer for publication-quality tables, covering common customizations and best practices.

1. Installation and Basic Usage

First, you need to install and load the stargazer package.

# Install if you haven't already
# install.packages("stargazer")
# Load the package
library(stargazer)
# --- Simulate some data for demonstration ---
set.seed(123)
n <- 100
data <- data.frame(
  x1 = rnorm(n),
  x2 = runif(n, 0, 10),
  x3 = sample(c("A", "B", "C"), n, replace = TRUE, prob = c(0.3, 0.4, 0.3)),
  treatment = factor(sample(c("Control", "Treated"), n, replace = TRUE)),
  y1 = 2 + 0.5*rnorm(n) + 1.2*rnorm(n) + rnorm(n),
  y2 = 5 + 0.8*rnorm(n) - 0.3*runif(n, 0, 10) + 0.7*rnorm(n) + rnorm(n)
)# Create some regression models
model1 <- lm(y1 ~ x1 + x2 + treatment, data = data)
model2 <- lm(y2 ~ x1 + x2 + treatment + x3, data = data)
model3 <- lm(y1 ~ x1 * x2 + treatment, data = data) # Model with interaction

Basic `stargazer` Command

The most basic command takes one or more model objects.

# Output to console (text format)
stargazer(model1, model2, model3, type = "text")
# Output to an HTML file (useful for websites or quick preview)
stargazer(model1, model2, type = "html", out = "regression_table.html")# Output to a LaTeX file (standard for academic publications)
# This will create a .tex file that you can include in your main LaTeX document.
stargazer(model1, model2, model3, type = "latex", out = "regression_table.tex")

2. Key Customization Options for Publication-Quality Tables

Publication-quality tables often require specific formatting, meaningful labels, and the inclusion/exclusion of certain statistics. stargazer offers extensive control over these aspects.

2.1. Changing Labels and Titles

title: The table's main title.

dep.var.labels: Labels for the dependent variables. These appear at the top of the columns.

dep.var.caption: An overall caption for the dependent variables section.

covariate.labels: Labels for the independent variables (coefficients).

column.labels: Labels for the columns themselves (if you want something different from the dependent variable labels, e.g., "Model 1", "Model 2").

add.lines: Add custom rows at the bottom of the table (e.g., for fixed effects, controls, specific notes).

notes: Footnotes for the table.

notes.align: Alignment for footnotes ("l", "c", "r").

notes.label: Label for the notes section (default is "Notes:").

stargazer(model1, model2, model3,
          type = "latex",
          title = "Regression Results: Predicting Y1 and Y2",
          dep.var.labels = c("Dependent Variable: Y1", "Dependent Variable: Y2", "Dependent Variable: Y1"),
          dep.var.caption = "Outcome Variables",
          covariate.labels = c("Predictor 1 (X1)",
                               "Predictor 2 (X2)",
                               "Treatment (Treated vs Control)",
                               "Factor X3 (Level B)",
                               "Factor X3 (Level C)",
                               "Predictor 1 * Predictor 2"),
          column.labels = c("Model A", "Model B", "Model C"), # Can be useful if dep.vars are the same
          notes = c("Standard errors in parentheses.",
                    "Significance levels: * p < 0.1, ** p < 0.05, *** p < 0.01."),
          notes.align = "l",
          out = "labels_example.tex")

2.2. Controlling Statistics Display

omit: Character vector of variable names to omit from the table. You can use regular expressions.

omit.stat: Character vector of statistics to omit (e.g., "f", "rsq", "adj.rsq", "n", "ser").

omit.table.layout: More granular control over the table's bottom section layout. Use ="n" to remove N, ="s" for SE, ="r" for R-squared, ="f" for F-stat, etc.

star.cutoffs: Change significance levels (default is c(0.1, 0.05, 0.01)).

se: Include standard errors (default is TRUE).

p: Include p-values (default is TRUE).

t: Include t-statistics (default is FALSE).

report: Control what to report in each cell, e.g., report="vc*p" for variable, coefficient, p-value. Default is vcsp.

stargazer(model1, model2, model3,
          type = "latex",
          title = "Regression Results with Custom Stats",
          covariate.labels = c("Predictor 1 (X1)", "Predictor 2 (X2)", "Treatment (Treated)"),
          omit = c("treatmentTreated", "x3"), # Omits 'treatmentTreated' and 'x3' levels (if present)
          omit.stat = c("f", "ser"), # Omits F-statistic and Residual Standard Error
          omit.table.layout = "n", # Omits N (Number of Observations)
          star.cutoffs = c(0.05, 0.01, 0.001), # New significance levels
          add.lines = list(c("Fixed Effects", "No", "No", "No")), # Example of adding info for all models
          out = "stats_example.tex")
# To specifically remove *only* the P-values, you can use p=FALSE
stargazer(model1, type = "latex", p = FALSE, out = "no_p_value.tex")
# To show t-statistics instead of P-values:
stargazer(model1, type = "latex", p = FALSE, t = TRUE, out = "show_t_stat.tex")# Customizing the cell report (e.g., Coefficient (Std. Error))
stargazer(model1, type = "latex",
          report = "vc", # v=variable name, c=coefficient, s=standard error, p=p-value, t=t-statistic
          out = "report_vc.tex")

2.3. LaTeX Specific Formatting

For type = "latex", these arguments are crucial for professional tables:

header: FALSE will only output the tabular environment, not a full LaTeX document. This is almost always what you want when including in a main document.

table.placement: Where LaTeX should try to place the table (e.g., h!, htbp).

label: A LaTeX label for referencing the table (e.g., \label{tab:myresults}).

font.size: Controls the font size ("tiny", "scriptsize", "footnotesize", "small", "normalsize").

align: Set to TRUE to use the tabular* environment, which distributes column space evenly.

no.space: Reduces vertical spacing between rows.

df: Degrees of freedom for t-statistics (for small samples, important).

single.row: If TRUE, stargazer tries to fit standard errors on the same line as coefficients (can be cramped).

stargazer(model1, model2, model3,
          type = "latex",
          title = "Regression Results: Publication Ready",
          dep.var.labels = c("Y1", "Y2", "Y1"),
          covariate.labels = c("X1", "X2", "Treatment (Treated)", "X3 (B)", "X3 (C)", "X1 x X2"),
          omit.stat = c("f", "ser"),
          star.cutoffs = c(0.05, 0.01, 0.001), # 3 stars for p < 0.001
          add.lines = list(c("Interactions", "No", "No", "Yes"),
                           c("Control Vars", "X1, X2, Trt", "X1, X2, Trt, X3", "X1, X2, Trt")),
          notes = "Standard errors in parentheses. Significance levels: * p < 0.05, ** p < 0.01, *** p < 0.001.",
          notes.align = "l",
          header = FALSE, # Only output the tabular environment
          table.placement = "!htbp", # LaTeX placement preference
          label = "tab:regression_results", # LaTeX label for referencing
          font.size = "footnotesize", # Smaller font for a compact table
          align = TRUE, # Use tabular* for better column alignment
          no.space = TRUE, # Reduce vertical space
          out = "publication_table.tex")

2.4. Numeric Formatting

digits: Number of decimal places for coefficients and standard errors.

decimal.mark: Character for the decimal separator (e.g., "." or ",").

digit.separator: Character for thousands separator (e.g., "" for none, "," for comma).

stargazer(model1,
          type = "latex",
          digits = 3, # Show 3 decimal places
          decimal.mark = ",", # Use comma as decimal separator
          digit.separator = ".", # Use dot as thousands separator
          out = "numeric_format.tex")

2.5. Handling Factor Variables and Interactions

stargazer automatically handles factor variables and interaction terms from lm objects.

Factor Levels: It will list each level (except the reference level) with its coefficient.

Interactions: It will typically use Var1:Var2 as the label. You can relabel these using covariate.labels.

# Example with interaction (model3 defined earlier)
stargazer(model3,
          type = "latex",
          title = "Model with Interaction Term",
          dep.var.labels = "Y1",
          covariate.labels = c("X1", "X2", "Treatment (Treated)", "X1 $\\times$ X2"), # Use LaTeX for symbols
          out = "interaction_table.tex")

3. Advanced Tips and Best Practices

Always use type = "latex" for publications. Then, include the generated .tex file in your main LaTeX document using \input{regression_table.tex}.

Crucial: Set header = FALSE when using \input. You only want the tabular environment, not a full document.

Meaningful Labels: Don't rely on R's default variable names (e.g., x1, factorA, log(x)). Use covariate.labels and dep.var.labels to make them human-readable and consistent with your paper.

Less is More: Omit unnecessary statistics (e.g., F-statistic if not relevant, R-squared for fixed-effects models if you're reporting adjusted R-squared or AIC/BIC). Use omit.stat or omit.table.layout.

Consistency: Keep the formatting, star cutoffs, and label styles consistent across all tables in your paper.

Fixed Effects: If your model includes fixed effects (e.g., lm(y ~ x + factor(ID))), you typically don't want to show all the fixed effect coefficients.

Use omit = "factor\$ID\$" (or omit = "ID" if it's just the variable name) to hide them.

Then, use add.lines to explicitly state that fixed effects were included.

# Example with fixed effects
    data$panel_id <- factor(sample(1:10, n, replace = TRUE))
    model_fe <- lm(y1 ~ x1 + x2 + treatment + panel_id, data = data)    stargazer(model_fe,
              type = "latex",
              title = "Model with Fixed Effects",
              covariate.labels = c("X1", "X2", "Treatment (Treated)"),
              omit = "panel_id", # Omit all coefficients for 'panel_id'
              add.lines = list(c("Panel Fixed Effects", "Yes")),
              out = "fixed_effects_table.tex")

Use add.lines for Additional Information: This is very flexible for adding rows like "Cluster Robust SEs", "Study Effects", "Sample Restrictions", etc.

LaTeX Symbols: For special characters or math in labels (e.g., $\beta_1$ , $\times$ ), use standard LaTeX notation within your string (e.g., covariate.labels = c("X1", "X2", "X1 $\\times$ X2")).

Outputting Multiple Tables: If you have many models, consider creating several stargazer calls for different groups of models, or using add.lines to clearly delineate sections within one large table.

4. Complete Example (Putting It All Together)

Let's create a "final" publication-ready table.

# Re-define models for clarity if needed, or use the ones from above
model1 <- lm(y1 ~ x1 + x2 + treatment, data = data)
model2 <- lm(y2 ~ x1 + x2 + treatment + x3, data = data)
model3 <- lm(y1 ~ x1 * x2 + treatment, data = data)
stargazer(model1, model2, model3,
          type = "latex",                     # Output type: LaTeX
          out = "final_regression_table.tex", # Output file name
          # Table title and label
          title = "Regression Results for Dependent Variables Y1 and Y2",
          label = "tab:final_results",
          # Column and variable labels
          column.labels = c("Model 1", "Model 2", "Model 3 (Interaction)"),
          dep.var.labels = c("Dependent Variable: Y1", "Dependent Variable: Y2", "Dependent Variable: Y1"),
          covariate.labels = c("Predictor 1",
                               "Predictor 2",
                               "Treatment (Treated)",
                               "Factor X3 (Level B)",
                               "Factor X3 (Level C)",
                               "Predictor 1 $\\times$ Predictor 2"), # LaTeX for interaction symbol
          # Statistics and display options
          omit.stat = c("f", "ser"),        # Omit F-statistic and Residual Standard Error
          star.cutoffs = c(0.05, 0.01, 0.001), # Custom significance levels (for *, **, ***)
          digits = 3,                       # Three decimal places for coefficients
          align = TRUE,                     # Use tabular* for better alignment
          no.space = TRUE,                  # Reduce vertical spacing
          header = FALSE,                   # Don't output a full LaTeX document header
          table.placement = "!htbp",        # LaTeX table placement
          # Add custom rows for additional information
          add.lines = list(
            c("Fixed Effects", "No", "No", "No"),
            c("Controls", "X1, X2, Treatment", "X1, X2, Treatment, X3", "X1, X2, Treatment")
          ),          # Notes/Footnotes
          notes.align = "l",
          notes = c("Standard errors in parentheses. ",
                    "Significance levels: * p < 0.05, ** p < 0.01, *** p < 0.001.")
)

What the finalregressiontable.tex file would look like (excerpt):

% You would include this in your main .tex file with \input{final_regression_table.tex}
\begin{table}[!htbp] \centering
  \caption{Regression Results for Dependent Variables Y1 and Y2}
  \label{tab:final_results}
  \footnotesize
\begin{tabular*}{\textwidth}{@{\extracolsep{\fill}}lccc}
\\[-1.8ex]\hline
\hline \\[-1.8ex]
 & \multicolumn{3}{c}{\textit{Dependent Variable:}} \\
\cline{2-4}
\\[-1.8ex] & \multicolumn{1}{c}{Y1} & \multicolumn{1}{c}{Y2} & \multicolumn{1}{c}{Y1} \\
\\[-1.8ex] & \multicolumn{1}{c}{(1)} & \multicolumn{1}{c}{(2)} & \multicolumn{1}{c}{(3)} \\
\\[-1.8ex] & \multicolumn{1}{c}{Model 1} & \multicolumn{1}{c}{Model 2} & \multicolumn{1}{c}{Model 3 (Interaction)} \\
\hline \\[-1.8ex]
Predictor 1 & -0.067 & 0.021 & 0.021 \\
            & (0.134) & (0.103) & (0.138) \\
            & & & \\
Predictor 2 & 0.022 & -0.030 & 0.057 \\
            & (0.022) & (0.017) & (0.023) \\
            & & & \\
Treatment (Treated) & 0.179 & -0.014 & 0.045 \\
            & (0.245) & (0.188) & (0.252) \\
            & & & \\
Factor X3 (Level B) & & 0.089 & \\
            & & (0.231) & \\
            & & & \\
Factor X3 (Level C) & & -0.169 & \\
            & & (0.232) & \\
            & & & \\
Predictor 1 $\times$ Predictor 2 & & & -0.003 \\
            & & & (0.020) \\
\hline \\[-1.8ex]
Fixed Effects & No & No & No \\
Controls & X1, X2, Treatment & X1, X2, Treatment, X3 & X1, X2, Treatment \\
\hline \\[-1.8ex]
Observations & 100 & 100 & 100 \\
R$^{2}$ & 0.012 & 0.038 & 0.015 \\
Adjusted R$^{2}$ & -0.018 & -0.012 & -0.026 \\
\hline
\hline \\[-1.8ex]
\textit{Notes:} \\
\textit{\footnotesize Standard errors in parentheses. } \\
\textit{\footnotesize Significance levels: * p < 0.05, ** p < 0.01, *** p < 0.001.} \\
\end{tabular*}
\end{table}

This comprehensive approach should equip you with the tools to generate publication-quality regression tables using stargazer for most academic and research needs! Remember to always compile your main LaTeX document to see the final output.

{DateTime.UtcNow:HH:mm}

GrokxAI

I need to create publication-quality tables for my regression results. Help me: 1) Use stargazer

I'll help you create publication-quality tables using the stargazer package in R. Here's a comprehensive guide:

1. Basic Setup and Installation

First, install and load the package:

# Install (run once)
install.packages("stargazer")# Load the package
library(stargazer)

2. Basic Regression Example

Let's start with a simple example using built-in data:

# Load example data
data(mtcars)
# Create regression models
model1 <- lm(mpg ~ hp, data = mtcars)
model2 <- lm(mpg ~ hp + wt, data = mtcars)
model3 <- lm(mpg ~ hp + wt + drat, data = mtcars)# Create basic table
stargazer(model1, model2, model3, 
          type = "text",
          title = "Regression Results: Miles Per Gallon",
          dep.var.labels = "MPG (miles per gallon)",
          column.labels = c("Simple", "Two Predictors", "Three Predictors"),
          covariate.labels = c("Horsepower", "Weight", "Rear Axle Ratio"))

3. High-Quality LaTeX Output

For publication quality, use LaTeX output:

stargazer(model1, model2, model3,
          type = "latex",
          title = "Determinants of Fuel Efficiency",
          dep.var.labels = "Fuel Efficiency (MPG)",
          column.labels = c("Model 1", "Model 2", "Model 3"),
          covariate.labels = c("Engine Horsepower", "Vehicle Weight", "Rear Axle Ratio"),
          column.separate = c(1, 1, 1),
          digits = 3,
          digits.extra = 0,
          star.char = c("*", "**", "***"),
          star.cutoffs = c(0.05, 0.01, 0.001),
          notes = "Standard errors in parentheses. * p<0.05, ** p<0.01, *** p<0.001",
          out = "regression_table.tex")

4. HTML Output for Reports

For HTML documents or R Markdown:

stargazer(model1, model2, model3,
          type = "html",
          title = "Regression Analysis Results",
          style = "ajps",  # American Journal of Political Science style
          dep.var.caption = "Dependent Variable:",
          omit.stat = c("ser", "f"),
          single.row = TRUE,
          notes.append = FALSE,
          notes = c("Robust standard errors in parentheses.",
                   "Year and industry fixed effects included."),
          out = "regression_table.html")

5. Advanced Customization

Customizing Variable Names and Formatting

stargazer(model1, model2, model3,
          type = "latex",
          title = "Fuel Efficiency Models",
          
          # Variable labels
          dep.var.labels = "Miles per Gallon",
          covariate.labels = c("Horsepower", 
                             "Weight (1000 lbs)", 
                             "Rear Axle Ratio"),
          
          # Statistical outputs
          omit.stat = c("rsq", "adj.rsq", "f"),  # Remove R-squared
          add.lines = list(c("Controls", "No", "Yes", "Yes"),
                          c("Observations", nrow(mtcars), nrow(mtcars), nrow(mtcars))),
          
          # Formatting
          digits = 3,
          digits.extra = 2,
          no.space = TRUE,
          intercept.bottom = TRUE,
          
          # Styling
          style = "default",
          font.size = "footnotesize",
          
          # Notes and labels
          notes = "Standard errors clustered by cylinder type.",
          notes.align = "l",
          notes.append = FALSE,
          
          out = "custom_table.tex")

6. Handling Multiple Datasets and Interactions

For more complex models with interactions:

# Create a more complex dataset
data(iris)
iris$Species <- as.factor(iris$Species)
# Multiple regressions
model_interaction <- lm(Sepal.Length ~ Sepal.Width * Petal.Width, data = iris)
model_robust <- lm(Sepal.Length ~ Sepal.Width + Petal.Length + Species, data = iris)stargazer(model_interaction, model_robust,
          type = "latex",
          title = "Iris Sepal Length Models",
          column.labels = c("Interaction Model", "Main Effects"),
          
          # Custom labels for interaction terms
          covariate.labels = c("Sepal Width", "Petal Width", 
                             "Sepal Width × Petal Width"),
          
          # Handle factors automatically
          omit = c("Speciesversicolor", "Speciesvirginica"),  # Omit dummy base levels
          omit.labels = "Yes",
          omit.stat = c("ser"),
          
          # Add custom lines
          add.lines = list(c("Species FE", "No", "Yes"),
                          c("R-squared", 
                            round(summary(model_interaction)$r.squared, 3),
                            round(summary(model_robust)$r.squared, 3))),
          
          out = "iris_analysis.tex")

7. Key stargazer Options

Option	Purpose	Example
`type`	Output format	`"text"`, `"latex"`, `"html"`, `"text"`, `"html"`
`title`	Table title	`"My Regression Results"`
`dep.var.labels`	Dependent variable label	`"Income (log)"`
`column.labels`	Column headers	`c("Model 1", "Model 2")`
`covariate.labels`	Variable names	`c("Age", "Education")`
`omit`	Variables to omit	`c("constant")`
`omit.stat`	Statistics to omit	`c("ser", "f")`
`digits`	Decimal places	`2`
`star.cutoffs`	Significance levels	`c(0.10, 0.05, 0.01)`
`style`	Table style	`"default"`, `"ajps"`, `"aer"`, `"ieee"`
`out`	Output file	`"table.tex"`

8. Example: Publication-Ready Panel Regression

# Simulated panel data example
set.seed(123)
panel_data <- data.frame(
  id = rep(1:100, 5),
  time = rep(1:5, each = 100),
  y = rnorm(500),
  x1 = rnorm(500),
  x2 = rnorm(500),
  treatment = rbinom(500, 1, 0.3)
)
# Fixed effects model
library(plm)
panel_model <- plm(y ~ x1 + x2 + treatment, 
                   data = panel_data, 
                   index = c("id", "time"), 
                   model = "within")
# Pooled OLS
pooled_model <- lm(y ~ x1 + x2 + treatment, data = panel_data)stargazer(pooled_model, panel_model,
          type = "latex",
          title = "\\textbf{Panel Data Regression Results}",
          dep.var.labels = "\\textit{Dependent variable:} Outcome Variable",
          column.labels = c("Pooled OLS", "Fixed Effects"),
          
          # Custom formatting for panel data
          covariate.labels = c("Explanatory Variable 1",
                             "Explanatory Variable 2",
                             "Treatment"),
          
          # Panel-specific options
          omit.stat = c("rsq", "adj.rsq"),
          add.lines = list(c("Individual FE", "No", "Yes"),
                          c("Time FE", "No", "No"),
                          c("\\# Observations", 
                            nrow(panel_data), nrow(panel_data))),
          
          # Professional styling
          style = "aer",
          font.size = "small",
          table.placement = "H",
          label = "tab:panel_regression",
          
          # Significance stars
          star.char = c("", "", "", "* ", "** ", "*** "),
          star.cutoffs = c(0.10, 0.05, 0.01),
          
          notes = c("Standard errors clustered at individual level in parentheses.",
                   "* $p<0.10$, ** $p<0.05$, *** $p<0.01§§§CODEBLOCK6§§§quot;),
          
          out = "panel_regression.tex")

9. Tips for Publication Quality

Always use type = "latex" for journals

Set style to match journal requirements ("ajps", "aer", "apsr", etc.)

Use covariate.labels to create readable variable names

Add add.lines for important model details (controls, FE, etc.)

Include informative notes about standard errors and significance levels

Use digits = 3 and digits.extra = 0 for clean decimals

Set omit.stat to remove unnecessary statistics

Use single.row = TRUE for cleaner coefficient presentation

This approach will give you professional, publication-ready tables that meet most social science journal standards. The key is customizing the labels and notes to match your specific research context and journal requirements.

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Analysis

This comparison demonstrates the different approaches each AI model takes when responding to the same prompt. Here are the key differences observed:

Response Characteristics

ChatGPT: Provides a direct response with 57 sentences.

Gemini: Provides a direct response with 293 sentences.

Grok: Provides a direct response with 117 sentences.

Key Takeaways

Each model brings unique strengths to this type of query
Response styles vary significantly between models
Consider your specific use case when choosing between these models

Try This Comparison Yourself

Want to test these models with your own prompts? Visit SNEOS.com to compare AI responses side-by-side in real-time.

This comparison was generated using the SNEOS AI Comparison ToolPublished: October 15, 2025 | Models: ChatGPT, Gemini, Grok