marginaleffects

Compute marginal effects, predictions, and comparisons from R regression models.

37|1|Updated Feb 20, 2026

Search Tags:#regression #R #effects #predictions #comparisons #model interpretation

Ready to run

npx skills add https://github.com/letitbk/claude-academic-setup --skill marginaleffects-letitbk

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: marginaleffects
Source: https://github.com/letitbk/claude-academic-setup/tree/main/skills/marginaleffects
Command: npx skills add https://github.com/letitbk/claude-academic-setup --skill marginaleffects-letitbk

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill facilitates computation of marginal effects, predictions, and comparisons from various statistical models in R, streamlining effect analysis.

Core Features & Use Cases

Effect Computation: Calculate average marginal effects for continuous and categorical variables in regression models.
Contrasts and Comparisons: Generate factor contrasts, continuous contrasts, and subgroup analyses.
Use Case: Researchers can determine how a one-unit increase in an independent variable influences the predicted outcome across different groups, supporting interpretability of model results.

Quick Start

Use the marginaleffects skill to compute the average marginal effects from your fitted regression model for interpretation.

Frequently Asked Questions about marginaleffects

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I compute marginal effects for continuous and categorical variables in R regression models?▼

You compute marginal effects in R by deriving average marginal effects for continuous and categorical variables from fitted regression models. This method streamlines effect analysis by calculating how a one-unit increase in an independent variable influences the predicted outcome.

What is the best way to generate contrasts and subgroup comparisons from a fitted R model?▼

Generating contrasts and subgroup comparisons involves calculating factor contrasts, continuous contrasts, and subgroup analyses from your R model. This process enhances model interpretability by clarifying differences across various groups within your data.

Can I use this method to interpret predictions across different regression types in R?▼

Yes, you can interpret predictions across various regression types in R. The method provides an efficient way to derive predictions and comparisons, enhancing model interpretability for researchers in social, behavioral, or health sciences.

How do average marginal effects help analyze the impact of independent variables on predicted outcomes?▼

Average marginal effects help analyze impact by determining how a one-unit increase in an independent variable influences the predicted outcome. This supports the interpretability of model results across different groups in social and health sciences research.

Do I need specific dependencies to calculate predictions and comparisons in R?▼

No specific dependencies are required to calculate predictions and comparisons in R. The method integrates smoothly with existing R modeling workflows, allowing you to compute effects directly from your fitted regression models without additional packages.