Detecting multicollinearity

In this exercise, you will check for multicollinearity among all variables by using the Variance Inflation Factor (VIF). You can calculate the VIF using the vif() function from the car package.

The VIF values are available in the GVIF column of the output and are usually printed in the exponential format. If you are not familiar with this format, you can use the format() function:

sample_vif_value <- 2.213e+10
format(sample_vif_value, scientific = FALSE)

"22130000000"

Deze oefening maakt deel uit van de cursus

HR Analytics: Predicting Employee Churn in R

Cursus bekijken

Oefeninstructies

Load the car package.
Check for multicollinearity in the model (multi_log) you built in a previous exercise.
Which variable has the highest VIF? Assign the variable name as a string to highest.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Load the car package
___

# Model you built in a previous exercise
multi_log <- glm(turnover ~ ., family = "binomial", data = train_set_multi)

# Check for multicollinearity
___

# Which variable has the highest VIF?
highest <- ___

Code bewerken en uitvoeren