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How is GVIF calculated for categorical variables?Also is there any other way to detect multi co-linearity of categorical variables?

I was tring to find a way to remove the redundant categorical variables as features. I believe GVIF would give high value for the redundant/multicollinear categorical variables. Please let me know if ...
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0answers
230 views

GVIF output in R

After fitting a logistic regression model m I've run vif() on it and been given the following output ...
0
votes
0answers
338 views

Multicollinearity in the data with categorical variables

I want to calculate the vif to check for multicollinearity in my data set. I read that a values of >10 tells me that I could have a problem with multicollinearity in my data set. I run an ols ...
1
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0answers
87 views

How to deal with high VIF and multicollinearity between numerical and categorical values?

I'm performing a multiple linear regression about the price of alcohol. Two of the predictors are the categorical variable 'type of alcohol', the levels being 'beer', 'wine', 'spirits' and the ...
3
votes
1answer
4k views

How can we calculate the variance inflation factor for a categorical predictor variable when examining multicollinearity in a linear regression model?

For example, if we have the linear regression model: $$E(y) = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \beta_3 x_3 $$ where $ x_1 =\begin{cases} 1 & \mbox{if level 2} \\ 0 & \mbox{otherwise} \...
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0answers
223 views

Weeding out multi-collinear categorical variables

I have a huge amount of features that are categorical variables and I'm trying to find a system for weeding out categorical variables that are close to being multicollinear. Is vif a reasonable ...
5
votes
1answer
10k views

How to test for multicollinearity among dummy explanatory variables?

I have a Box-Cox regression where the explanatory variables are almost all dummy variables. If I want to see if there is multicollinearity among them, what would be an appropriate test? Do variance ...