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Everything seems like it should work... Sorry I couldn't be of more help...

And the JASP data format is in the wide format? Very weird indeed...

JASP uses BayesFactor under the hood, so they should produce the same results... The only thing I can think of is if the data is a repeated measures design, and the ID random intercept is missspecified somewhere..

Hi Lior, Bayes factors are the ratio of P(DataM) (the likelihood), not P(MData) (the posterior probability of the model). Hope that helps!

Hi Flaihai, If you are interested in accounting for knowledge gained in the first study, you can use a replication Bayes factor (for a simple application you can read DOI:10.3758/s134280181092x), assuming this is a direct / exact replication. Goo…

It would see that the matrix is organized with row as the numerator and column as the denominator ( this does seem counterintuitive...), so 31735.222 represents how much more likely H1 is compared to H2. You can also see that the PMP (Posterior mo…

What you want is to orderrestrict your H1 model. This can be done in one of the following: Newest version of JASP has a BAIN module seems to do just this (but is in BETA) with "model constrains". More info here >> Write some custom …

But maybe replace order of magnitude on base 10, with order of magnitude on basee (which is basically the classical cutoffs for BFs)..

> but still human I resent your assumptions, sir! But yes, you are right  it would affected my interpretation somewhat 😞 (But that is a change of X4, what you described was a change of <X2, no?)

As you mention these fluctuations are minor (I would say that a change less than an order of magnitude is not substantial for a BF that is a ratio, after all). What to do? Tread lightly  I think it is reasonable to explain in ms that due to instabi…

Hi Chris, JASP uses the afex package for (rm)AN(C)OVA. Among other things, afex (1) makes sure factors have effect coding, and (2) used type III errors (whereas summary.aov uses type II), both of which affect estimates and significances in unbalance…

The reason you have and exclusion BF is that you have  under Bayes Factor  marked BF01. If you want the inclusion BF mark BF10. Note that these are reciprocal  BF01 = 1/BF10, and BF_inc = 1/BF_exc.

Generally, yes  but I'm not sure you want to use whichModels = "bottop"  it is advised to stick with the defaults here (whichModels = "withmain") (see here). Also you can't really get a BF for an F test  as BF are always compa…

These look really good! It is okay if I use them for class? Just one note: on the left panel, the gray points aren't exactly the pointnull, they represent the likelihood of the null value on the alternative prior and posterior distributions. You ca…

The posterior distribution(s) does not equal what you wrote, but the posterior odds do. Note sure what you mean by: That's because the approach doesn't permit defining the alternative hypothesis as, specifically, the degree of secondorder uncertain…

But the Bayes factor does not support updating with respect to H and I. Instead, it involves updating with respect to H and J, where J is not the same hypothesis as I The BF is based on model I completely  it simply incorporated information regardi…

Okay, never mind  I got it now  reading the original post more closely ^_^

Looking at the code, I understand that models with interactions are compared only to models with all the main effects. But why is this?

Hi Richard, I think you might have mixed the prior distribution with the prior odds. Say you want to test if there is or isn't a difference between two groups. For this you would construct two models: Model A, in which the difference is nonzero. Mo…

Your conclusions seem correct. One option would be to report these results. Another option might be to fit and test your specified model (one main effect and the interaction) in R: library(BayesFactor) data(puzzles) # example of 2*2 data # This i…

Thanks Richard! This is very helpful!

Can you post a screen grab of the models and effects tables?

As a footnote, the violation of the assumption of Sphericity might also explain why posthoc ttests gave such strong results  as these tests do not have the Sphericity assumption.

Hi Ronen, Looking at the (frequentist) RMAnova table you provided, that little 'a' marking for the "cong" effect (and its interaction with "go_nogo") indicates your data violation of model assumption of Sphericity. This, in comb…

With a large `N`, a small effect (almost 0) might be "nearly significant", but in a Bayesian framework such a small effect with a large N would more indicative of `H0` being true.

Seems like (as an unintended side effect) inferBF also enables ROPE analysis.

If you want some of the actual JASP code: https://github.com/jaspstats/jaspdesktop/tree/development/JASPEngine/JASP/R https://github.com/jaspstats/jaspdesktop/tree/development/JASPEngine/JASPgraphs/R

For many supported models, you can also the tidyverse approach, with the ggeffects package.

Last I checked, all Bayesian analyses (ANOVA and regression) in JASP (which are done with BayesFactor) are actually linermixed models, so unbalanced designs should not be a problem.

You're 100% right  BF10 = 1/BF01 I always prefer to write the actual values so that they are humaninterpolate (which ever is larger than 1). e.g. it's easier to understand that H1 was 4.784 times less likely than H0 (BF01 = 4.784), than that H1 …