EJ
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- EJ
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Comments
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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…
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* yes you should be able to add another column (variable); just double-click the data and use your preferred spreadsheet editor, press save, and the JASP file ought to be updated. * wrt replicating your analyses on a different data set, have you tri…
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Completely correct! E.J.
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Hi MSB, The correction for multiplicity is usually always through the prior odds. This is even the case in parameter estimation, where you have to spread out your prior mass across more options (imagine a discrete parameter space with an increasing …
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Well I am prodding people, as you are prodding me. But it will be more effective if you prod on our GitHub page, because then everybody in the team gets to see it
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It means that the data have changed the odds in favor of models that include the predictor by a factor of 19. When BFincl = .30, you can interpret this as BFexcl = 1/.30 = 3.33, some change in the odds but nothing to get worked up about. E.J.
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Hi MSB, * The "U" in BF10,U stands for "uncorrected" (the upcoming version will mention this explicitly in the table footnote) -- so yes, they are the same. * Yes, posterior odds = prior odd * BF * Yes, the correction for multip…
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Hi Jonas, In principle, the question of the presence of an effect is independent of the strength/size of that effect. I would argue that the prior odds can still be based on outcomes for earlier experiments, but then in terms of Bayes factors that …
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Hi Jonas, It is indeed a little confusing, because "prior" means different things. On the level of models, the prior p(H1) means "what is the relative plausibility of H1?"; on the level of parameters within a model it means &qu…
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BF inclusion is similar to a regular BF except that it compares two classes of models, one class with the factor of interest and one without. So you start with prior probabilities on the models; comparing the prior probability with vs without the fa…