Standardize data first

scaler = StandardScaler() X_scaled = scaler.fit_transform(X)

PCA with varimax rotation

fa = FactorAnalyzer(n_factors=4, rotation='varimax') fa.fit(X_scaled)

Get factor loadings

loadings = fa.loadings_

Get component scores for each observation

scores = fa.transform(X_scaled)


## Workflow for Attribution Analysis

When using PCA for contribution analysis with predefined categories:

1. **Combine ALL variables first**, then do PCA together:
```python
# Include all variables from all categories in one matrix
all_vars = ['AirTemp', 'NetRadiation', 'Precip', 'Inflow', 'Outflow',
            'WindSpeed', 'DevelopedArea', 'AgricultureArea']
X = df[all_vars].values

scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# PCA on ALL variables together
fa = FactorAnalyzer(n_factors=4, rotation='varimax')
fa.fit(X_scaled)
scores = fa.transform(X_scaled)

Interpret loadings to map factors to categories (optional for understanding)
Use factor scores directly for R² decomposition

Important: Do NOT run separate PCA for each category. Run one global PCA on all variables, then use the resulting factor scores for contribution analysis.

Interpreting Factor Loadings

Loadings show correlation between original variables and components:

Loading	Interpretation
> 0.7	Strong association
0.4 - 0.7	Moderate association
< 0.4	Weak association

Example: Economic Indicators

import pandas as pd
from sklearn.preprocessing import StandardScaler
from factor_analyzer import FactorAnalyzer

# Variables: gdp, unemployment, inflation, interest_rate, exports, imports
df = pd.read_csv('economic_data.csv')
variables = ['gdp', 'unemployment', 'inflation',
             'interest_rate', 'exports', 'imports']

X = df[variables].values
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

fa = FactorAnalyzer(n_factors=3, rotation='varimax')
fa.fit(X_scaled)

# View loadings
loadings_df = pd.DataFrame(
    fa.loadings_,
    index=variables,
    columns=['RC1', 'RC2', 'RC3']
)
print(loadings_df.round(2))

Choosing Number of Factors

Option 1: Kaiser Criterion

# Check eigenvalues
eigenvalues, _ = fa.get_eigenvalues()

# Keep factors with eigenvalue > 1
n_factors = sum(eigenvalues > 1)

Option 2: Domain Knowledge

If you know how many categories your variables should group into, specify directly:

# Example: health data with 3 expected categories (lifestyle, genetics, environment)
fa = FactorAnalyzer(n_factors=3, rotation='varimax')

Common Issues

Issue	Cause	Solution
Loadings all similar	Too few factors	Increase n_factors
Negative loadings	Inverse relationship	Normal, interpret direction
Low variance explained	Data not suitable for PCA	Check correlations first

Best Practices

Always standardize data before PCA
Use varimax rotation for interpretability
Check factor loadings to name components
Use Kaiser criterion or domain knowledge for n_factors
For attribution analysis, run ONE global PCA on all variables

pca-decomposition

PCA Decomposition Guide

Overview

When to Use PCA

Basic PCA with Varimax Rotation