EJ
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- EJ
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Comments
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Hi Caeline, Yes, that's correct. There are no verbal guidelines for BF10's of 1 million. You might invent your own category -- I usually call BF's in that range "overwhelming". In general though, the verbal labels are just heuristics, and …
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If you have a large effect, or a small effect but with large sample size, BFs can be huge. Even with N=1, you can get a BF of infinity. Example: toss a coin; H0 says theta = 1 (i.e., coin has heads on both sides); throw the coin once and observe tai…
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Yes, you are correct. The "across all models" BFincl is also not in favor of adding the interaction, although the strength of evidence is not as compelling as for the matched models. Perhaps the interaction term features in a few models wi…
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Do the best model has Position and irrelValue. The second best has only Position, and the third best has only irrelValue. The first model with an interaction enters at place 5, and is a factor of 3.293 worse than the best model. This is echoed by t…
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The "Baws" method proposed by Sebastiaan should give the same result as "Analysis of effects" with the "matched models" option. Do you get a different outcome? E.J.
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I'll pass the message on to our network expert! E.J.
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The error is so small that I would not even report it. E.J.
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Hi Ester, The one-sided BF is calculated by departing from the two-sided BF and then adding a correcting factor. The correction factor is close to its maximum value, which might produce the problem. I'll bring it to the addition of the programming …
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I need a little more context, perhaps a concrete example?
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Hi Chris, The desirable properties of the Cauchy hold for any scaling. The value of 1 was suggested by Jeffreys but this is not a principled point. The value of 1/sqrt(2) was suggested in the BayesFactor R package to be more reasonable (i.e., more …
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Hi Anoop, One of our recent multi-million $ grants is on applications to medicine; so yes, those analyses are definitely on the agenda! Cheers, E.J.
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Dear Clarisse, OK, let's tackle these one at a time: (Quote) Yes. (Quote) BF10 is just 1/BF01, so they provide exactly the same information. If BF10 is 0.1, say, it feels awkward to say "the data are 0.1 times more likely under H1 than under…
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Hi Merel, I don't think you can, at least not right now. If the five conditions were between-subjects you'd just have different sample sizes in each of the conditions. But here you have a within-design, and this complicates things. From a Bayesian …
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Hi Aram, I think this is one of the few issues where Richard and I have a different opinion. I would argue that there are multiple models to consider, and it is best to average over them. In JASP, you can do this by ticking "Effects", and…
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https://twitter.com/AlexanderLyNL/status/918197338841190400 https://twitter.com/AlexanderLyNL/status/918195429652750337
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Right now you'll have to create a separate column, as we do not have filtering functionality (yet). I recall that Alexander had a demonstration how this could be easily done, let me ask him... E.J.
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Hi wendt, For a detailed explanation see for instance, on my website, the paper Wagenmakers, E.-J., Love, J., Marsman, M., Jamil, T., Ly, A., Verhagen, A. J., Selker, R., Gronau, Q. F., Dropmann, D., Boutin, B., Meerhoff, F., Knight, P., Raj, A., v…
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MSB is spot on. See also, on my website: Ly, A., Etz, A., Marsman, M., & Wagenmakers, E.-J. (2017). Replication Bayes factors from evidence updating. Manuscript submitted for publication. URL: https://psyarxiv.com/u8m2s/ E.J.
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The posthocness expresses itself through the prior model probabilities. The BF remains the same. Cheers, E.J.
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Not yet. Perhaps he will respond when you send him a personal Email? Cheers, E.J.
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Hi Anja, Usually you get this error whenever you try to estimate a model that includes interactions but not the corresponding main effects. If you just drag the variables into their boxes this should not happen. So my first question would be, when …
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Hi Thorsten, Thanks for presenting the case so clearly. Yes, I think you are spot on. It is interesting that there is so much evidence against the interaction -- I bet this is because you have 5 levels of disparity. As an aside, this is valuable in…
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I'll need some time to digest this. Will get back to you later.
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Hmm OK, that is a rather big effect of leaving out this single participant. Of course, you could also argue "how it is that the p-value only changes from .09 to .14 when I leave out this huge outlier in my relatively small data set?" but t…
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That's interesting. I'll have to look at this a little later (some deadlines now, remind me if I haven't responded in a week), but some of the discrepancies may be due to violations of assumptions (homogeneity of variances). I'll take a look.
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OK I confirmed my hunch. If you upgrade to the latest version JASP will automatically recognize the NA and show a "." in its place. Cheers, E.J.
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Depends on your JASP version. If you use 0.8.4 (the latest one), JASP should automatically recognize the "NA" as missing value (you can set this in the preference menu). If JASP does not recognize the NA as missing value and instead classi…
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Hi Kasia, That's a remarkable test. It does make sense to me to present the quantile that the patient represents in the control population, and perhaps some uncertainty that comes with that quantile (for instance through bootstrapping or a Bayesian…
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I think that Richard Morey will have more insightful comments. I'll attend him to your post. E.J.
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Hi Alon, * Convergence is most likely not a problem for these models. * The Bayesian ANOVA in JASP is simply the Bayesian mixed model. So you should be able to get the same result out of JASP as you get out of BayesFactors. * I find this difference…