What are VIF values in R?
The most common way to detect multicollinearity is by using the variance inflation factor (VIF), which measures the correlation and strength of correlation between the predictor variables in a regression model. The value for VIF starts at 1 and has no upper limit.
Which package contains VIF in R?
Several packages in R provide functions to calculate VIF: vif in package HH, vif in package car, VIF in package fmsb, vif in package faraway, and vif in package VIF. The number of packages that provide VIF functions is surprising given that they all seem to accomplish the same thing.
How to calculate Vif for a regression model in R?
To illustrate how to calculate VIF for a regression model in R, we will use the built-in dataset mtcars: First, we’ll fit a regression model using mpg as the response variable and disp, hp, wt, and drat as the predictor variables: We can see from the output that the R-squared value for the model is 0.8376.
How to calculate the VIF of a predictor variable?
To visualize the VIF values for each predictor variable, we can create a simple horizontal bar chart and add a vertical line at 5 so we can clearly see which VIF values exceed 5: Note that this type of chart would be most useful for a model that has a lot of predictor variables, so we could easily visualize all of the VIF values at once.
What should the value of the VIF be?
The value for VIF starts at 1 and has no upper limit. A general rule of thumb for interpreting VIFs is as follows: A value of 1 indicates there is no correlation between a given predictor variable and any other predictor variables in the model.
How is Vif used to detect collinearity?
VIF can be used to detect collinearity (Strong correlation between two or more predictor variables). Collinearity causes instability in parameter estimation in regression-type models. The VIF is based on the square of the multiple correlation coefficient resulting from regressing a predictor variable against all other predictor variables.