How To Get Summary Statistics On The Bottom Rows Of A Merged Regression Table?

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In this comprehensive guide, we delve into the intricacies of the {gtsummary} package in R, focusing on how to effectively generate summary statistics, specifically R-squared and log-likelihood, and append them to the bottom of a merged regression table. This is a crucial skill for researchers and analysts who need to present a clear and concise comparison of different regression models, such as a baseline model versus an interaction model. The gtsummary package offers a user-friendly interface for creating publication-ready tables, and mastering its advanced features like adding summary statistics enhances the interpretability and impact of your work.

Understanding the Need for Summary Statistics in Regression Tables

When presenting regression results, it's essential to provide not only the coefficient estimates and their significance but also summary statistics that reflect the overall fit of the model. Key summary statistics include R-squared, which indicates the proportion of variance in the dependent variable explained by the model, and log-likelihood, which provides a measure of how well the model fits the data. By including these statistics at the bottom of a merged regression table, you allow readers to quickly assess and compare the performance of different models. For instance, comparing the R-squared values of a baseline model and an interaction model can reveal whether the inclusion of interaction terms significantly improves the model's explanatory power. Similarly, comparing log-likelihood values can help determine which model provides a better fit to the data. In the following sections, we'll explore how to leverage the gtsummary package to seamlessly incorporate these vital summary statistics into your regression tables.

Step-by-Step Guide to Adding Summary Statistics to Merged Regression Tables with Gtsummary

To effectively add summary statistics like R-squared and log-likelihood to the bottom of your merged regression tables using the gtsummary package, we'll walk through a detailed, step-by-step process. This approach ensures clarity and allows you to replicate the process for your own analyses. First, we'll begin by fitting the regression models you want to compare. This might involve fitting a baseline model and then a more complex model, such as one with interaction terms. Once the models are fit, the next step is to extract the R-squared and log-likelihood statistics. R provides functions to readily access these statistics from the model objects. With these values in hand, we'll then use gtsummary's flexible table modification functions to add these statistics as new rows at the bottom of the table. This involves creating custom rows that display the R-squared and log-likelihood values for each model. Finally, we'll merge the tables for the different models, ensuring that the summary statistics are neatly aligned at the bottom, providing a comprehensive comparison of model performance. By following these steps, you can create informative and publication-ready tables that clearly present your regression results alongside key summary statistics.

1. Fitting the Regression Models

First, you need to fit the regression models you want to compare. This usually involves fitting a baseline model and then a more complex model, such as one with interaction terms. For example, let's say you want to compare a model with only main effects to a model that includes an interaction term. You would use R's lm() function (for linear models) or glm() function (for generalized linear models) to fit these models.

# Load necessary libraries
library(gtsummary)
library(tidyverse)

model_baseline <- lm(mpg ~ disp + hp + wt, data = mtcars)



model_interaction <- lm(mpg ~ disp + hp + wt + disp:hp, data = mtcars)

In this example, we are using the mtcars dataset. The baseline model (model_baseline) predicts mpg (miles per gallon) based on disp (displacement), hp (horsepower), and wt (weight). The interaction model (model_interaction) adds an interaction term between disp and hp to see if the effect of displacement on fuel efficiency varies depending on the horsepower of the car.

2. Extracting R-squared and Log-Likelihood

Once the models are fit, you need to extract the R-squared and log-likelihood statistics. R provides functions to readily access these statistics from the model objects. For R-squared, you can use the summary() function, and for log-likelihood, you can use the logLik() function.

# Extract R-squared for the baseline model
r_squared_baseline <- summary(model_baseline)$r.squared


log_likelihood_baseline <- logLik(model_baseline)



r_squared_interaction <- summary(model_interaction)$r.squared



log_likelihood_interaction <- logLik(model_interaction)

Here, we extract the R-squared value using summary(model)$r.squared and the log-likelihood using logLik(model). These values will be used in the next step to add them to the gtsummary table.

3. Adding Summary Statistics to the Gtsummary Table

With the R-squared and log-likelihood values extracted, the next step is to incorporate them into your gtsummary table. The gtsummary package provides flexible functions to modify tables, including adding custom rows. We'll use the add_rows() function to append these statistics to the bottom of the table. This involves creating a data frame or a tibble containing the summary statistics and then using add_rows() to add these as new rows to the table. This ensures that the summary statistics are neatly displayed alongside the regression coefficients, providing a comprehensive overview of the model's performance.

# Create gtsummary table for the baseline model
table_baseline <- tbl_regression(model_baseline) 


summary_stats_baseline <- tibble::tibble(
term = c(