Use R studio to answer the problem

diabetes=read.csv("https://raw.githubusercontent.com/AliUofC/Data601/refs/heads/main/diabetes.csv", header = TRUE) str(diabetes) head(diabetes)

Split the dataset into two parts - training and test.The training part contains 400 individuals from the "neg" class and 200 units from the "pos" class. The test part contains the rest data. Let "diabetes" be the response variable, get a logistic regression model based on the training part using all the explanatory variables, which variables contribute to the "pos" result in a negative way? Check the "variance inflation factor" for each coefficient (e.g., using "vif" function from the package "car"). Do you detect multicollinearity?