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Generalized Additive Models

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Fit and diagnose GAMs in R.

Data & Analytics#r#gam#model diagnostics#mgcv#smooth terms#concurvity
Authorntluong95
Version1.0.0
Installs0

System Documentation

What problem does it solve?

This Skill provides a standardized approach to fitting and diagnosing Generalized Additive Models (GAMs) in R, specifically using the mgcv package, helping users effectively model non-linear relationships in their data.

Core Features & Use Cases

  • Flexible Modeling: Fit models with univariate and multivariate smooth terms using various basis functions.
  • Model Diagnostics: Assess smooth term adequacy, concurvity, and effective degrees of freedom.
  • Use Case: When analyzing the non-linear relationship between air pollution exposure and health outcomes over time, use this Skill to fit a GAM with smooth terms for both exposure and time, and then check for concurvity between these smooths.

Quick Start

Use the Generalized Additive Models skill to fit a GAM model with a quasipoisson family and smooth terms for 'time' and 'temperature' to the 'health_data' dataframe.

Dependency Matrix

Required Modules

None required

Components

references

💻 Claude Code Installation

Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.

Please help me install this Skill:
Name: Generalized Additive Models
Download link: https://github.com/ntluong95/agent-skills-statistics/archive/main.zip#generalized-additive-models

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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