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Generally I'd advocate reporting both analyses. The BF inclusion formula is similar to BF10 in that it quantifies the change from prior odds to posterior odds. But in the BF inclusion case, those odds are computed for all the models that contain the variable of interest vs those that do not.
As far as the interpretation goes, yes, something like that -- I'd explicitly indicate that you contrasted the models with and without the variable of interest.
Thank you very much for your answer. I read your interesting discussion about BF Inclusion with Sebastiaan (here http://forum.cogsci.nl/index.php?p=/discussion/2996/odd-bayesian-repeated-measures-seems-biased-against-interactions-and-in-favor-of-main-effects) and his presentation of what he called "Baws Factors" (here : https://www.cogsci.nl/blog/interpreting-bayesian-repeated-measures-in-jasp). Is it right to say that Baws Factors are just BF Inclusion computed using a different bayesian model averaging procedure than those used in JASP?
Thanks you very much
The "Baws Factors" should be in the next version of JASP; they are computed by neglecting certain models. So the procedure is the same, but what differs is the set of models that is averaged over.
Thank you very much.
Have a nice day
Hi E.J., I was happy to see the last updates to JASP including BMA across matched models. However, when I run my analysis with JASP I don't find the same BFincl as when I compute the BAWS factors by hand. I think that I have probably made a mistake when computing them by hand. I would like to understand where my mistake is. Could you provide the exact formula used to compute the BAWS in JASP please? Thanks a lot
Sure, happy to oblige. I'll ask Tim to send the relevant code, or post it here.
I talked about that with Sebastiaan. I attach a file with my JASP results and my by-hand results. I obtained the by-hand results by applying the procedure proposed by Sebastiaan to compute BAWS Factors (https://www.cogsci.nl/blog/interpreting-bayesian-repeated-measures-in-jasp).
Thanks again for your help
It's a little tricky to give an excerpt of the code as it uses calculated results from other functions.
Let's give it a try though!
I attached the code to this post in a .zip file.
To demonstrate what the calculated results from the other functions look like I also include a .RData file.
This .RData file was generated with a Bayesian RM ANOVA performed on the JASP Bugs dataset.
The column names look a little funky, because they contain (escaped) unicode characters.
Normally the columns are base-64 encoded, but for clarity purposes I decoded them.
If you mimic the structure of the objects
effectNamesyou should be able to run the code.
Hopefully you'll find it helpful!
If you have any other questions do not hesitate to ask.
Could you maybe send me the .jasp file itself, Nicolas?
(firstname.lastname@example.org if you want to keep it private)
Hi Tim, thanks for your help and for the script. I will send you the .jasp file.
Omitting the prior inclusion probabilities,
IV 1 * IV 2:
Obviously there is some discrepancy because of rounding, but it seems to match to me.
Thanks Tim. However, for IV 1 * IV 2, I don't understand how your sum of P(M|data) for in-models and for out-models are not equal to 0.868 and 0.003 respectively. JASP indicate Incl. BF = 250.453 for this effect whereas my by-hand calculation indicates 285.33. Intuitively, I thought that the discrepency should be smaller if it is merely due to rounding.
Not 100% sure, but these high BFs translate into very small probabilities, where rounding can make a big difference. To check, you could take an example where the BFs are modest.
Thanks for your answer E.J., I think I finally got it. Your explanation makes sense with the very small discrepancies observed for the BF associated with the main effects.
Thanks to both of you. Best regards
Glad it makes sense to you now
I indeed made a little mistake with the copy-pasting earlier.
It should be
IV 1 * IV 2:
To demonstrate how strong the effect of rounding is with low probabilities (as EJ mentioned); IV 1 * IV 2 is (roughly) bounded between:
Lower: 0.868 / 0.0034 = 255
Upper: 0.868 / 0.0025 = 347
Hi Tim, thanks very much. It is crystal-clear thanks to you guys.
I am doing a Bayesian RM-ANOVA in R using the "BayesFactor" package. I am not an absolute expert, so I used JASP with the same data to check whether I did everything correctly. Luckily, all Bayes factors were roughly the same. Then I saw the inclusion BF across matched models in JASP (suggested by Sebastiaan Mathôt). I know that the "BayesFactor" package is the basis for JASP functionality. However, I could not find a function or an easy way to calculate the inclusion BF across matched models in R. When I googled that, I found the present forum thread. I looked at the "matchedModels.R" code that was posted here. However, I have problems extracting the effects.matrix, model$interaction.matrix, and effectNames from my BFBayesFactor object in R. Stated differently, I have an object of the Bayesian RM-ANOVA in R. Assuming that I have this object and the "matchedModels.R" code that was provided above, how can I get the effects.matrix, model$interaction.matrix, and effectNames and, ultimately, the inclusion BF across matched models in R?
Thank you in advance for your help!
Ah, this is something that Tim did for us.
Tim is on a trip around the world and may rejoin us, but that will take a few more months. Of course our JASP/R code online should demonstrate how this is done...
I've previously posted some of my custom code here in the forum (here>>) that includes a function for computing inclusion BFs.
Thanks a lot MSB! That is exactly what I was looking for. I have one question, though: Is there a fundamental reason why you decided to calculate the log of the inclusion BF?
No particular reason - just found that is makes it easier to do other calculation with it later (which I do sometimes).
You can set the print to show the regular BFs:
Or you can extract them yourself
Thanks MSB, I forgot this for a moment
Great. That solves everything! Thank you.