Decomposing R squared or VIF In the context of multi-regression, I am wondering if there is a way to decompose $$VIF_i = 1/(1-R_i^2)$$ where $R_i^2$ is the r squared obtained from the regression of dependent variable = i and independent variables are all other factors.

I want to decompose $VIF_i$ or $R_i^2$ into individual factors to see how much each individual factor contributes to the $VIF_i$ or $R_i^2$

Someone recommended using the square of partial correlation coefficient and that value is linearly related to $R_i^2$. My undestanding is that partial correlation coefficient measures the correlation between two variables, holding the other variables constant.  Is this a viable option?
I suggest calculating R-squared and VIF for each permutation of variable combinations.

Also consider the fact that variable interactions may influence your R-squared and VIF values. Depending on what programming language you are using there are many ways to build interactions into your regression model, and you can iteratively test for effects from there.