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Before running or building a model, ho can we check on the multicollinearity between different covariates in GLM model in R?

I know that SAS Proc MIXED procedure gives a column for VIF which is very easy but how to check in R?

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    $\begingroup$ There are several possibilities to calculate the variance inflation factor. You could try the vif() function from the car package. $\endgroup$
    – smillig
    Jul 25, 2014 at 11:54
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    $\begingroup$ Multicollinearity is a property of the regressors, not the model, so you don't need to look for "multicollinearity in GLM" as opposed, say, to "multicollinearity in OLS". In addition, there are other measures of multicollinearity than VIF, like the condition indices and variance decomposition proportions of Belsley, Kuh & Welsch, so it would be good if you could edit your question - are you specifically interested in the VIF, or generally in detecting multicollinearity in R? (I also voted to close and move to stackoverflow.com, since this seems to be specifically about R.) $\endgroup$ Jul 25, 2014 at 12:28
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    $\begingroup$ @Stephan Alternatively, we could take the appearance of this question on CV as a request for information about checking for multicollinearity among regressors (regardless of the computing platform), in which case your comment would be the start of good answer :-). $\endgroup$
    – whuber
    Jul 25, 2014 at 13:15
  • $\begingroup$ A few words on multicollinearity in general are here. The multicollinearity tag may also be helpful. $\endgroup$ Jul 28, 2014 at 13:44
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    $\begingroup$ @whuber What about a GLMER? Would there also be another way to calculate VIF for it? $\endgroup$ May 26, 2016 at 13:44

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