Statistical Diagnostics calculator

Variance Inflation Factor (VIF) calculator

Calculate Variance Inflation Factor (VIF) and Tolerance to detect multicollinearity among predictors in multiple linear regression models.

Run this test live in StatLab npm i @statlab/core

When to use it

Use when evaluating multiple regression models with correlated predictors.

Required Inputs

  • Predictor matrix X
  • Target variable Y

Mathematical Formula

VIF_j = 1 / (1 - R_j²)

Reporting Cautions

  • VIF > 5 indicates moderate multicollinearity; VIF > 10 indicates severe multicollinearity.
  • Multicollinearity inflates standard error estimates of regression coefficients.

Code Snippets (Python, R, TypeScript)

Python (SciPy / Statsmodels) 
from statsmodels.stats.outliers_influence import variance_inflation_factor
vif = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]
print(vif)
R Language 
library(car)
vif(lm_model)
TypeScript (@statlab/core) 
import { calcVif } from '@statlab/core';
const vifArr = calcVif(xMatrix);

Developer Use Cases & Production Integrations

  • Diagnosing feature correlation redundancies in predictive ML models.
  • Validating independence of telemetry metrics in VoxelPulse regression modules.

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