How to deal high multicollinearity during multiple imputation

Question

[Phylicia]

Hi， I conducted a multiple imputation based on the tutorial of Virginia University. https://data.library.virginia.edu/getting-started-with-multiple-imputation-in-r/

However, the error report is:

imp2 <- mice(merge2, maxit = 5, 
             predictorMatrix = predM, 
            method = meth, print=TRUE)

Error in edit.setup(data, setup, ...) : 
  `mice` detected constant and/or collinear variables. No predictors were left after their removal.

When I establish print=TRUE，it does not show when the algorithm stops because of multicollinearity. How can I do trouble shoot?

Thank you very much!