R Variance Inflation Factor Calculator

Calculator Input

Example Data Table

This sample shows four predictors. The first row contains headers.

In this example, x1 and x2 move together strongly. Their VIF values may become large.

Formula Used

Variance Inflation Factor measures how much a predictor is explained by other predictors. For each predictor, the calculator runs an auxiliary regression.

VIFj = 1 / (1 - Rj²)

Tolerancej = 1 / VIFj

Rj² = R squared from regressing predictor j on all other predictors

A VIF of 1 means no inflation. A VIF between 2.5 and 5 may need review. A VIF above 10 often signals serious multicollinearity.

How to Use This Calculator

1. Paste a numeric dataset into the input box.
2. Select the delimiter used in your data.
3. Check the header option if the first row has names.
4. Enter selected columns when only some predictors are needed.
5. Choose a VIF review threshold.
6. Press the calculate button.
7. Review VIF, tolerance, correlation, status, and advice.
8. Export the results as CSV or PDF.

Understanding Variance Inflation Factor

Why VIF Matters

Variance Inflation Factor helps detect multicollinearity in regression work. Multicollinearity happens when predictors share too much information. This can make coefficients unstable. It can also make standard errors larger. A model may look useful. Yet single predictor effects may become hard to trust. VIF gives a direct warning signal. It shows how much coefficient variance is inflated.

How the Calculator Works

The calculator treats every selected column as a predictor. It then tests one predictor at a time. The selected predictor becomes the response. The remaining predictors become explanatory variables. An internal least squares regression is solved. The resulting R squared value is used in the VIF formula. Higher R squared means higher shared movement. That creates a larger VIF score.

Reading the Results

A VIF near 1 is usually healthy. It means the predictor is not explained by others. Moderate values deserve attention. High values may require action. You may remove duplicate variables. You may combine similar measurements. You may use domain judgment. You may also compare alternate models.

Practical Modeling Notes

VIF is not a final model rule. It is a diagnostic measure. Some fields allow higher values. Designed experiments may create correlation by structure. Financial and survey data can also show overlap. Review VIF beside theory, sample size, and prediction goals. For R users, the logic matches common VIF workflows. It helps before reporting coefficients. It also helps before explaining variable importance.

FAQs