Bayes factor for equality of correlation coefficients?

Comments

• EJ

  November 2015 edited 2:36AM

  Hi Florian,

  Note that JASP has a slightly different implementation of the correlation test than the one described in Wetzels et al. Specifically, JASP uses the original test as proposed by Jeffreys (for an explanation see http://www.ejwagenmakers.com/inpress/JeffreysToPTests.pdf).
  We have not yet implemented a test between two correlations, but it is in the works. One way is to assume independent priors on both rate parameters; the other way is to assume a prior on the difference (and do a Savage-Dickey test on that difference). This would be facilitated by first transforming the correlation, perhaps using the Fisher z. The trick here is to define a good default prior. But, like I said, we have not done this yet and JASP does not do it.

  Cheers,
  E.J.
• flo

  December 2015 edited 2:36AM

  Hi EJ,

  thanks for your quick response!

  So, could I Fisher-z-transform both correlation coefficients, then subtract them and transform the difference back. Then test whether the difference is different from 0 using either JASP or the R-code from Ruud and your 2012 paper?

  Or would that end up testing something different?
• EJ

  December 2015 edited 2:36AM

  Hi Florian,
  I don't think it's that simple, unfortunately. Once you have a posterior distribution for the difference in correlation coefficients, you need to "Savage-Dickey" this against a prior on the difference. Note that the scale on the difference between two correlation coefficients is not from -1 to 1, so you can't test it as you would a regular correlation. Of course if you have correlations for every participant individually (based on some repeated measures) then you can use ANOVA (but this is an unlikely scenario).
  Cheers,
  E.J.
• flo

  December 2015 edited January 2016

  Hi EJ,

  unfortunately, this is not a repeated-measures situation.

  Thank you for your quick and clear answers! I guess I'll have to wait until JASP includes this feature then.

  Take care!

  • Florian
• flo

  January 2016 edited 2:36AM

  Hi EJ,

  I want to quickly revive this thread to ask you another question. I recently asked the same question to someone else and they responded with this:

  ---

  You can compute Bayes factors, as described in the paper, by comparing any two models.
  Lets define two different models:

  M_x: Z = b_xz X
  M_y: Z = b_yz Y
  

  Similar to the linked paper, we can reframe the correlation test as a linear regression test. Particularly since in your case we only need to compare two vectors at a time so this is equivalent to testing that b_xz != b_yz. So

  BF = p(Z | M_x) / p(Z | M_y)
  

  And then we have an identical setup to the partial correlation test (equation 15) except that the number of regression coefficients is fixed at 1.

  # From Wetzels & Wagenmakers (2012):
  corcor.bf=function(r1,r2,n){
    int=function(r,n,g){
      (1+g)^((n-2)/2)*(1+(1-r^2)*g)^(-(n-1)/2)*
        g^(-3/2)*exp(-n/(2*g))};
  
    bf10= integrate(int,lower=0,upper=Inf,r=r1,n=n)$value/
      integrate(int,lower=0,upper=Inf,r=r2,n=n)$value
    return(bf10)
  }
  

  so...

  set.seed(10010)
  n <- 80
  a <- rnorm(n)
  b <- rnorm(n)
  c <- a + rnorm(n, sd=2)
  
  corcor.bf(cor(a,c), cor(a,b), 80)
  BF ~= 86
  

  ---

  Is this a valid approach? I understand the conceptual part of the response and it makes sense to me but am not qualified to judge the modification of your (and Ruud's) function.

  Thank you for your time!

  • Florian
• EJ

  January 2016 edited 2:36AM

  I have not thought this through very deeply but I don't think it will work. Specifically, you want to know whether a correlation r1 (for experiment 1) is the same as r2 (for experiment 2). But experiment 1 and 2 provide different data. You have to compare two accounts for the complete data set, one with a single r and one with two r's. This is also the case when you use the regression framework: a single beta for the two sets of data, or two beta's.
  E.J.
• flo

  January 2016 edited 2:36AM

  Hi EJ,

  once again, thank you for your straightforward response. I see the difference between your regression framework and the one outlined above and why the latter might be problematic. Damn, I thought I stumbled across a neat solution...

  Thanks!

  • Florian
• jploenneke

  December 2019

  I noticed that this post is from a few years ago...i was curious if there was anything within JASP to do this now

  
  

  http://comparingcorrelations.org/

  
  

  that link is for comparing correlation from the Fishers Z transformation...is there a similar Bayesian test?
• EJ

  December 2019

  We're still working on it, and I am pinging the person responsible every time this comes up :-)
• jploenneke

  December 2019

  I appreciate your prompt response. I look forward to what you guys have in store for the future.
• PolinaArb

  February 2020

  Hello everyone,

  I found this post while looking for a Bayesian test of (in)equality of correlation coefficients. Seems like it's still under construction, so I'm posting this to confirm the interest in it :)

  I'm also interested in a Bayesian alternative for Jennrich test, to test differences between two correlation matrices - didn't manage to find anything similar to it anywhere.

  Many thanks,

  Polina
• EJ

  February 2020

  Ah this is very interesting. I'll forward this to the team member tasked with this project!

  E.J.
• JulianG

  July 2020

  Hello,

  Given the script posted by John Kruschke:

  http://doingbayesiandataanalysis.blogspot.com/2017/06/bayesian-estimation-of-correlations-and.html

  I wonder whether there has been any progress about this feature in JAGS. Looking forward to using JAGS for this purpose :-)

  In the mean time (I know that working on this is both time and effort consuming), I would like to clarify a previous comment from this post. EJ previously stated in his comment about the savage-Dieckey" test:

  "Of course if you have correlations for every participant individually (based on some repeated measures) then you can use ANOVA (but this is an unlikely scenario)".

  This is my case: I've computed Bayesian corr. coefficients from 40 participants which underwent 4 conditions (A1, A2, B1, B2). I need to test whether the corr coefficient (A1, A2) differs in relation to corr. coefficient (B1, B2). I wonder how ANOVA would provide information on the difference of these Bayesian coefficients (Brho A1, A2 vs. Brho B1, B2).

  Best regards,

  
  

  Julian G,
• EJ

  March 2022

  Update

  This is one correct approach I believe: https://research.tilburguniversity.edu/en/publications/bayes-factor-testing-of-equality-and-order-constraints-on-measure

  We want to implement this in JASP but that project has yet to start.

  Cheers,

  E.J.