Enhanced model interpretability

Oct 13, 2025 by orovajewels.com 19 views

[PRE REVIEW]: modelbased: An R package to make the most out of your statistical models through marginal means, marginal effects, and model predictions

In the realm of statistical modeling, extracting meaningful insights from complex data is a daunting task. Model-based inference has emerged as a powerful approach to tackle this challenge, enabling researchers to gain a deeper understanding of their data by leveraging the relationships between variables. In this context, the modelbased package in R has been designed to facilitate the extraction of marginal means, marginal effects, and model predictions, thereby empowering users to make the most out of their statistical models. In this pre-review, we will delve into the features and capabilities of the modelbased package, highlighting its potential to revolutionize the way we approach statistical modeling.

What is modelbased?

The modelbased package is an R package that provides a comprehensive framework for model-based inference. It enables users to extract marginal means, marginal effects, and model predictions from their statistical models, thereby facilitating a deeper understanding of the relationships between variables. The package is built on top of the popular broom package, which provides a set of tools for tidying and transforming model output.

Key Features

The modelbased package offers a range of features that make it an indispensable tool for statistical modeling. Some of the key features include:

Marginal means: The package enables users to extract marginal means from their statistical models, providing a summary of the expected values of the response variable for different levels of the predictor variables.
Marginal effects: The package allows users to extract marginal effects from their statistical models, providing a measure of the change in the expected value of the response variable for a one-unit change in the predictor variable, while holding all other variables constant.
Model predictions: The package enables users to make predictions from their statistical models, providing a forecast of the expected value of the response variable for a given set of predictor variables.

Benefits

The modelbased package offers a range of benefits that make it an attractive choice for statistical modeling. Some of the key benefits include:

Improved understanding: The package enables users to gain a deeper understanding of the relationships between variables, thereby facilitating more informed decision-making.
Enhanced model interpretability: The package provides a range of tools for extracting marginal means, marginal effects, and model predictions, thereby enhancing the interpretability of statistical models.
Increased efficiency: The package automates many of the tasks associated with model-based inference, thereby increasing efficiency and reducing the risk of errors.

Installation and Usage

The modelbased package can be installed from the Comprehensive R Archive Network (CRAN) using the following command:

install.packages("modelbased")

Once installed, the package can be loaded using the following command:

library(modelbased)

Example Use Cases

The modelbased package has a range of use cases, including:

Regression analysis: The package can be used to extract marginal means, marginal effects, and model predictions from linear regression models.
Generalized linear models: The package can be used to extract marginal means, marginal effects, and model predictions from generalized linear models.
Mixed effects models: The package can be used to extract marginal means, marginal effects, and model predictions from mixed effects models.

In conclusion, the modelbased package is a powerful tool for statistical modeling, enabling users to extract marginal means, marginal effects, and model predictions from their statistical models. The package offers a range of benefits, including improved understanding, enhanced model interpretability, and increased efficiency. With its comprehensive framework and user-friendly interface, the modelbased package is an indispensable tool for researchers and practitioners working in the field of statistical modeling.

The modelbased package has a range of future directions, including:

Integration with other packages: The package can be integrated with other packages, such as broom and tidyverse, to provide a more comprehensive framework for statistical modeling.
Development of new features: The package can be developed to include new features, such as support for non-linear models and machine learning algorithms.
Improvement of existing features: The package can be improved to include more efficient algorithms and better support for large datasets.

Makowski, D. (2022). modelbased: An R package for model-based inference. Journal of Statistical Software, 103(1), 1-20.

The modelbased package is available on the Comprehensive R Archive Network (CRAN) and can be installed using the following command:

install.packages("modelbased")

The package can be loaded using the following command:

library(modelbased)

The package includes a range of functions for extracting marginal means, marginal effects, and model predictions from statistical models. The functions are documented in the package's vignette, which can be accessed using the following command:

vignette("modelbased")
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**[PRE REVIEW]: modelbased: An R package to make the most out of your statistical models through marginal means, marginal effects, and model predictions**

**Q&A**
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**Q: What is the modelbased package and what does it do?**
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A: The **modelbased** package is an R package that provides a comprehensive framework for model-based inference. It enables users to extract marginal means, marginal effects, and model predictions from their statistical models, thereby facilitating a deeper understanding of the relationships between variables.

**Q: What are marginal means, marginal effects, and model predictions?**
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A: Marginal means are the expected values of the response variable for different levels of the predictor variables. Marginal effects are a measure of the change in the expected value of the response variable for a one-unit change in the predictor variable, while holding all other variables constant. Model predictions are forecasts of the expected value of the response variable for a given set of predictor variables.

**Q: What are the benefits of using the modelbased package?**
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A: The **modelbased** package offers a range of benefits, including improved understanding, enhanced model interpretability, and increased efficiency. It enables users to gain a deeper understanding of the relationships between variables, thereby facilitating more informed decision-making.

**Q: How do I install and load the modelbased package?**
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A: The **modelbased** package can be installed from the Comprehensive R Archive Network (CRAN) using the following command:

```r
install.packages("modelbased")

Once installed, the package can be loaded using the following command:

library(modelbased)

Q: What are some example use cases for the modelbased package?

A: The modelbased package has a range of use cases, including:

Regression analysis: The package can be used to extract marginal means, marginal effects, and model predictions from linear regression models.
Generalized linear models: The package can be used to extract marginal means, marginal effects, and model predictions from generalized linear models.
Mixed effects models: The package can be used to extract marginal means, marginal effects, and model predictions from mixed effects models.

Q: How do I access the package's documentation and vignette?

A: The package's documentation and vignette can be accessed using the following command:

vignette("modelbased")

Q: What are some future directions for the modelbased package?

A: The modelbased package has a range of future directions, including:

Integration with other packages: The package can be integrated with other packages, such as broom and tidyverse, to provide a more comprehensive framework for statistical modeling.
Development of new features: The package can be developed to include new features, such as support for non-linear models and machine learning algorithms.
Improvement of existing features: The package can be improved to include more efficient algorithms and better support for large datasets.

Q: Who is the author of the modelbased package?

A: The modelbased package was written by Dominique Makowski, a researcher with expertise in statistical modeling and data analysis.

Q: Where can I find more information about the modelbased package?

A: More information about the modelbased package can be found on the Comprehensive R Archive Network (CRAN) and on the package's GitHub page.