EJ
About
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- EJ
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Comments
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Hi Senne, You were correct, and so is M above. It is the reviewer who has misunderstood. Cheers, EJ
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Hi M, You can view the effect of the Beta* prior widths by trying out a few values and ticking the plot option to view the results. Basically, the standard beta distribution is defined for a parameter ranging from 0 to 1 (check out Wikipedia for an…
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Hi M, Right now JASP does not do truncation on the beta prior. However, you can take the results and add the truncation after the fact. Here is how it works. JASP gives you the Bayes factor BF10 of a H1 model versus H0. You can then use a "tric…
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Hi Arni, In our modeling, the main effect is included whenever a specific factor appears in the interaction. This adheres tp the principle of marginality (https://en.wikipedia.org/wiki/Principle_of_marginality) and it is considered good modeling pr…
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Hi Bryan, Ah, BayesMed...I will leave that one up to Michele! :-) Cheers, E.J.
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Hi Marieke, I think it might take three months or so before this functionality is in. I will take it up with those responsible today. Note: the Verhagen paper does have R code, and so should the other replication BF papers we did (one on correlati…
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In my opinion, you have an independent multinomial. Participants are divided in two conditions, and the interest is in the differences in rate. We will provide more articles, tutorials, and books in the future! Cheers, E.J.
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That's awesome!! We'll look into making these available for the next release as well. E.J.
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The logic follows from the (streched) beta. I recall that the prior width of 0.5 generates a beta(2,2) stretched from -1 to 1 (because 2 = 1/0.5; the width is inversely related to the parameters of the beta). When you run an analysis you can tick th…
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I have not thought this through very deeply but I don't think it will work. Specifically, you want to know whether a correlation r1 (for experiment 1) is the same as r2 (for experiment 2). But experiment 1 and 2 provide different data. You have to c…
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Hi George, Thanks for your questions. First, the Bayesian ANOVA in JASP does require more in terms of displaying effect sizes -- this is work in progress. Also, in JASP the classical test provides a plot of the data, but the Bayesian echo does not;…
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Hi Nitzan, Yes, you can use the transitivity trick to compare the model with the interaction to the model that has the other terms but lacks the interaction. However, as you suggest, you can also designate those other terms as nuisance. These nuisa…
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Hi Markus, We are working on a series of books and manuals that explain exactly this. For now your best source for information is the papers that Richard and I have produced. We have published on Bayesian test for regression, ANOVA, t-tests, and co…
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Hi Pascal, JASP uses the correlation test as originally proposed by Jeffreys in 1961. It assumes a bivariate normal distribution and puts (by default) a uniform prior on rho. The philosophy behind the test is explained here (http://www.ejwagenmaker…
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Yes I think it's definitely worth it! The current result is suggestive and N is not that high. E.J.
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Hi Juliane, (1) The Bayes factor is close to 3 for the default prior, and the robustness check reveals that it is never much more, no matter how you set the prior width. As the pizza plot reveals, with BF=3 and equal prior odds you have a posterior…
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Hi Florian, I don't think it's that simple, unfortunately. Once you have a posterior distribution for the difference in correlation coefficients, you need to "Savage-Dickey" this against a prior on the difference. Note that the scale on th…
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Hi Florian, Note that JASP has a slightly different implementation of the correlation test than the one described in Wetzels et al. Specifically, JASP uses the original test as proposed by Jeffreys (for an explanation see http://www.ejwagenmakers.c…
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Hi Sebastiaan, Good points. However, I would not say that the prior is obtained/derived from the (observed!) data. As the name suggests, the prior is specified before the data are observed, and ideally it reflects our expectations about the size of…
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Hi Tamara, In most Bayes factor tests, all that matters is the prior on the parameter of interest. For the t-test, for instance, the parameter of interest is "effect size $\delta$". In the usual situation, there are two hypotheses: H0, wh…
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Hi Matti, Good points. (1) I have added "make help button more prominent" as a feature request via the JASP GitHub system. By the way, this is easy to do: just go to jasp-stats.org, to "development" and then to "feature re…
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Hi Guillon, OK. An entire discussion of all of the entries is perhaps going too far; Maarten Marsman and I will write a paper doing just that (for a special issue -- we hope to be done in a few weeks). Anyway, let me tell you what I would conclude.…
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Hi Guillon, Thanks for your question! With respect to the interpretation, it would indeed be good to add the image. With respect to the Cauchy prior, your are right, we did not implement this for the ANOVA. We discussed it recently and then decided …