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

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 …

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 …

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.

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 …

Apr 21, 2015 · I am running kmeans to identify clusters of customers. I have approximately 100 variables to identify clusters. Each of these variables represent the % of spend by a customer …

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 …

I cannot differentiate clearly between "interaction" and "collinearity" in multiple linear regression. For me these terms are related but not the same. I have searched …

Aug 11, 2013 · Rules of thumb may say that multicollinearity is a problem only if two variables correlate above, say, .9 or even more. If two of your latent variables correlated that much, or …