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contribution-analysis

Quantify relative factor contributions to a response variable using R² decomposition.

317|40|Updated Jan 21, 2025
Search Tags:#statistics#data-analysis#variance#pca#factor-analysis#r-squared
One-click install
npx skills add https://github.com/benchflow-ai/benchflow --skill contribution-analysis-benchflow-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: contribution-analysis
Source: https://github.com/benchflow-ai/benchflow/tree/main/tests/fixtures/skillsbench_slice/lake-warming-attribution/environment/skills/contribution-analysis
Command: npx skills add https://github.com/benchflow-ai/benchflow --skill contribution-analysis-benchflow-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, scikit-learn, factor_analyzer.

What problem does it solve?

This skill resolves the ambiguity of determining which variables most significantly impact a response outcome when dealing with complex, correlated datasets.

Core Features & Use Cases

  • R² Decomposition: Mathematically isolates the variance explained by individual factors.
  • Global PCA Integration: Uses varimax-rotated factor analysis to handle multicollinearity effectively.
  • Use Case: Use this when analyzing climate or economic data to identify which specific environmental or social drivers contribute most to a observed change in a target variable.

Quick Start

Use the contribution-analysis skill to calculate the relative importance of the variables in the current dataframe and export the dominant factor to output.csv.

Frequently Asked Questions about contribution-analysis

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

FAQPage Schema
How do I calculate the relative contribution of correlated variables to a response outcome?▼

To calculate the relative contribution of correlated variables, use R² decomposition to mathematically isolate the variance explained by individual factors and identify dominant drivers in complex datasets.

What is the best way to identify dominant drivers of variance in a multi-variable dataset?▼

The best way to identify dominant drivers of variance is applying varimax-rotated factor analysis to handle multicollinearity effectively and attribute observed changes to specific environmental or social factors.

Can I use pandas and scikit-learn to perform factor influence quantification on climate data?▼

Yes, you can use pandas, numpy, scikit-learn, and factor-analyzer to perform factor influence quantification on climate data and export dominant variable contributions to a CSV file.

Does R² decomposition work for analyzing economic data with multicollinearity?▼

R² decomposition works for analyzing economic data with multicollinearity by integrating varimax-rotated factor analysis, which mathematically isolates variance explained by individual correlated factors.

How do I export dominant factor results from a variance attribution analysis?▼

To export dominant factor results from a variance attribution analysis, run the statistical decomposition on your dataframe and save the identified dominant factors directly to output.csv.

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