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

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 …

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 …

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.

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 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 …

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

Jul 14, 2020 · These terms kind of confuse me because they all seem to imply a certain correlation. Confounder: influences dependent and independent variable Collinearity: to me …

Mar 13, 2015 · In my understanding, highly correlated variables won't cause multi-collinearity issues in random forest model (Please correct me if I'm wrong). However, on the other way, if I …