EJ
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That is correct E.J.
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Hi Richard, You can multiply the Bayes factor by the prior model odds (p(H1)/p(H2)) and this then gives you the posterior model odds. The BF gives you the extent to which the data should change your beliefs. So yes, the prior model odds matter, but…
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Yes, the Bayesian account for multiplicity is entirely in the prior model probabilities. To respond to your question: "Are you saying that we only need to adjust for multiplicity by correcting the prior odds?" Yes "In other words, …
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I recommend the following two: 1. Etz, A., Gronau, Q., Dablander, F., Edelsbrunner, P., Baribault, B. (in press). How to become a Bayesian in eight easy steps: An annotated reading list. For the Bayesian Statistics special issue in Psychonomic Bulle…
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I'd simply use the standard output from JASP. E.J.
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Dear Vadim, * You have BF10 = 0.175. This means that that BF01 = 1/0.175 = 5.71, meaning that the data are about 5.7 times more likely under H0 than under H1. This is support in favor of H0. I should not that BF10 = 0.99 would also have constitute…
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For each predictor, you can look at the posterior inclusion probability. What we will add in the next version is also the prior inclusion probability (right now this is only shown in a plot); the inclusion BF is the ratio between the posterior incl…
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You can try it out. I recall that the results are slightly different, and this is because the underlying models are not identical (i.e., the prior predictions about to-be-observed data are not the same).
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The posterior odds are for the effect being present, where the prior odds have been corrected for multiplicity (the post-hoc character of the test). The BF01,U gives the evidence in the data without correcting for multiplicity (the U stands for unco…
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Yes that makes perfect sense! (note that the BF says something about the evidence from the data -- if you want the probability of a hypothesis being true then you need to take the prior model plausibility into account). So the safest conclusion is t…
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Hi Mirjam, This looks like an issue for our GitHub page. I'll create the issue for you. Cheers, E.J.
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Are you using a binomial model, and do you want to use a beta prior? You can set the "a" and "b" parameters of the prior distribution. If you start from a uniform distribution (big if) then, after seeing 9/10 "successes"…
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Hi Andrew, Right now, you can't. In the future we aim to expand the output , as we have recently done for linear regression (up to 0.8.5., we only reported BFs; as of 0.8.6, we also have posterior distributions). Richard may now how you can use the…
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You should check yourself, there is no gold standard; it also depends on the numbers themselves -- you may not care whether the BF is one or two billion. I am not sure about the difference between "samples from the posterior" and "i…
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Thanks for bringing this to our attention. We now have a team member working to upgrade this functionality. Cheers, E.J.
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Yes, it is based on conting! It uses Reversible Jump MCMC, and under uniform prior probabilities for the models, the number of visits relates directly to the posterior probabilities (and this also given the BF). E.J.
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Hi Andrew, There's a series of papers on planning experiments until a BF crosses a bound. If you look on my website you'll see a few with Felix Schoenbrodt. This is the latest one: https://psyarxiv.com/aqr79 Cheers, E.J.
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For many regression/ANOVA analyses the integral only goes over one parameter, so full MCMC is not needed. I am not sure what the BayesFactor package does for repeated measures ANOVA though -- should be in the documentation. But yes, BayesFactor asse…
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I'll pass this on to the person who developed the meta-analysis module. E.J.
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Hi Jo, No, this has not been implemented yet. It would be straightforward to implement for the chi-square test, but for the ANOVA the problem is that the check may take a very long time. So we probably need to pick just a few points and plot the re…
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I am not 100% sure. The "real/royal" Bayesian way is to change the data-generating process, for instance by assuming that the data are t-distributed instead of normally distributed, or assuming . Some classical robust methods throw away da…
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Hi Mark, Thanks for your kind words, and thanks for reporting this issue. Could you perhaps send us your .jasp file, so we can reproduce the anomaly? It would be even better if you could post this on our GitHub page, as all the JASP team members th…
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Basically you just copy-paste Lavaan code. We are working on a video and gif to demonstrate the process.
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Jeffreys does mention this in his 1961 book, but I am not aware of a modern Bayes factor test. It is straightforward to do estimation using JAGS/Stan, but the test requires a bit more development. It is on our radar. E.J.
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Hi eniseg2, Right now we only have "classical" SEM in JASP; we will of course include the Bayesian echo at some point (through blavaan, probably). Cheers, E.J.
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Hi Kevin, JASP already provides effect sizes. To clarify, for the Bayesian methods JASP generally provides the following: (1) The Bayes factor, a single number that quantifies the evidence for the presence/absence of an effect; (2) A posterior dist…
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For the record, the answer by Richard is here: https://twitter.com/richarddmorey/status/956574998898139138 Richard: "if X is the t-test prior scale, then X/sqrt(2) is the ANOVA scale, and X/2 is the corresponding regression scale." You: …
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I would add the nuisance parameter (covariate) to the null model, and then interpret the outcome as you would do for the ANOVA. If you are also interested in the covariate itself, you can keep it out of the null model. If you send a screenshot of th…
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That's great to hear -- I must admit I personally have used the BayesFactor package mostly indirectly, through JASP. Thanks for posting the solution. Cheers, E.J.
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First and foremost, it is important to be aware that if IQ differs between the groups, adding it as a covariate does not "control" for it. ANCOVA was meant to be used when the covariate (IQ) is important, but does not differ between the gr…