How to deal with multicollinearity when performing variable selection? Ask Question Asked 13 years, 8 months ago Modified 6 years, 3 months ago

Mar 23, 2025 · I am new to the subject and only know from cross-sectional linear regression models that variance inflation factors (VIFs) can be a great way to detect multicollinearity in the set of …

Mar 2, 2021 · Thanks for the comment Patrick. I agree that removing multicollinearity before completing any regression will provide better results and more robust model (I saw better results from Lasso …

The wiki discusses the problems that arise when multicollinearity is an issue in linear regression. The basic problem is multicollinearity results in unstable parameter estimates which makes it very

Jul 15, 2021 · 12 Does it ever make sense to check for multicollinearity and perhaps remove highly correlated variables from your dataset prior to running LASSO regression to perform feature selection?

multicollinearity refers to predictors that are correlated with other predictors in the model It is my assumption (based on their names) that multicollinearity is a type of collinearity but not sure.

Apr 8, 2025 · I'm aware that one of SHAP's disadvantages is the precision of SHAP values in scenarios with multicollinearity because of the assumption of predictor independence.

The blogger provides some useful code to calculate VIF for models from the lme4 package. I've tested the code and it works great. In my subsequent analysis, I've found that multicollinearity was not an …

Nov 3, 2022 · Checking multicollinearity with generalized additive model in R Ask Question Asked 7 years, 2 months ago Modified 3 years, 1 month ago

Nov 15, 2023 · Could multicollinearity be messing things up here? After reading several things off and on this site, I'm considering running a linear regression in SPSS to get multicollinearity VIFs even …