In that case, you can just use vif(). The 'generalized VIF' is only
relevant when a factor variable has more than two levels and one wants to
compute a VIF that pertains to the whole factor, not just each of the
individual dummy variable. But if the factor only has two levels, then
there is only one dummy variable, so this is the same as GVIF.
Best,
Wolfgang
-----Original Message-----
From: Rafael Rios [mailto:biorafaelrm at gmail.com]
Sent: Friday, 19 June, 2020 16:35
To: Viechtbauer, Wolfgang (SP)
Cc: r-sig-meta-analysis at r-project.org
Subject: Re: [R-meta] Testing multicollinearity between categorical
predictors
Dear Wolfgang,
Yes, it is. Yes and no for each moderator. I am also evaluating their
interaction.
All the best,
Rafael.
Em sex., 19 de jun. de 2020 ?s 10:12, Viechtbauer, Wolfgang (SP)
<wolfgang.viechtbauer at maastrichtuniversity.nl> escreveu:
I am not sure I fully understand. Are you saying that the two moderators
have two levels each?
Best,
Wolfgang
-----Original Message-----
From: Rafael Rios [mailto:biorafaelrm at gmail.com]
Sent: Friday, 19 June, 2020 15:02
To: Michael Dewey
Cc: Viechtbauer, Wolfgang (SP); r-sig-meta-analysis at r-project.org
Subject: Re: [R-meta] Testing multicollinearity between categorical
predictors
Dear Michael,
Thank you for the reply. I am evaluating the biases arising from pooling
samples from different populations and periods on the average effect
Therefore, I included both pooling practices, and their interaction as
moderators. Each practice has two levels (yes and no).
Best wishes,
Rafael.
Em sex., 19 de jun. de 2020 ?s 09:50, Michael Dewey
<lists at dewey.myzen.co.uk> escreveu:
Dear Rafael
It is hard to answer here because we do not know what scientific problem
the referee thinks he or she has spotted which would be solved by such a
test. Being of a cynical world view I suspect neither does the referee
and this is a conditioned reflex like Pavlov's dog salivating at the
Are the two moderators of scientific interest to you or are you
including them so you can say that there is still residual heterogeneity
even after you did your best to explain it? In the latter case I would
suggest collinearity is irrelevant.
Michael
On 19/06/2020 13:36, Rafael Rios wrote:
Dear Wolfgang,
Thank you for the replay. I also thought about using VIF to evaluate
multicollinearity, but there is a lot of criticism about the
of VIF for categorical predictors. There is a variation called GVIF.
However, since the meta-analysis changes categorical predictors to
variables, I could not use it in R. I am not sure whether this is the
approach. Do you not other methods to evaluate or avoid potential
multicollinearity among categorical moderators?
Best wishes,
Rafael.
Em sex., 19 de jun. de 2020 ?s 05:40, Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer at maastrichtuniversity.nl> escreveu:
Dear Rafael,
I don't know what "testing" for multicollinearity would entail. One
examine the variance inflation factors with vif(). What VIF values are
considered "large" is debatable though.
Best,
Wolfgang
-----Original Message-----
From: Rafael Rios [mailto:biorafaelrm at gmail.com]
Sent: Wednesday, 17 June, 2020 2:28
To: Viechtbauer, Wolfgang (SP); r-sig-meta-analysis at r-project.org
Subject: Testing multicollinearity between categorical predictors
Dear Wolfgang and All,
Is there a good method for testing multicollinearity between
predictors in meta-regression? I ran a mixed-effects MLMA using two
categorical predictors and their interaction as moderators, but a
requested a test of multicollinearity. I did not find a good approach
solve this problem. Thank you in advance.
Best wishes,