EJ
About
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- EJ
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Comments
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Hi Spi, This is my take on the issue: interactions are inherently complex --they can take on all kinds of shapes-- and this means that they spread their predictions out relatively widely; this entails a relatively strong penalty for complexity. Mor…
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Hi Bill, I can have someone look into this, but it would certainly help us if you could provide a specific example so we have some concrete numbers to work with. Cheers, E.J.
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Hi Charles, Good question. I'll attend Richard to it. Cheers, E.J.
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Hi Inge, The ANOVA model is not so simple. I advice you to start with http://www.ejwagenmakers.com/inpress/RouderEtAlinpressANOVAPM.pdf We are working on a JASP Manual that will explain these in a language that is perhaps easier to understand, but t…
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Hi Eniseg, Best to take this issue to the GitHub pages, where you can interact directly with the programming team. We hope to release a new version soon, and if this is a bug we'd love to fix it asap. Cheers, E.J.
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Hi Guillaume, In Jeffreys's opinion, BFs in between 1/3 and 3 are "not worth more than a bare mention", and I agree. So that BF01 = 1.951 really doesn't tell us much. The effect of group is also not really compelling, so I would not lose s…
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Hi Guillaume, When you put "Time" and "Group" as nuisance, you are basically comparing the full model to the model with two main effects; that is, you are asking whether the inclusion of the interaction adds anything compared to…
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Good points. The 95% CI is an inferential measure and we didn't feel it belongs in descriptives. But I'll discuss this with the team. Making the "split by" option more general is highly desirable, we agree. E.J.
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These are all good points, I will pass them on to the people responsible. I think we want to stick with "log" instead of introducing "ln" (see also how R does this). Thanks for helping out with these great suggestions! Note that …
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Definitely. Not the next version (this is in a few weeks), but perhaps the one after that.
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Hi Biva, Yes, please send us your data set. You can just Email it to EJ.Wagenmakers@gmail.com and I'll forward it to Johnny. E.J.
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Suppose you compute a Spearman's rho and conduct the standard analysis on the ranks. Suppose you have three observations, and the ranks are the same in the two variables. This yields: X Y 1.00000... 1.00000... (rank 1 for X and Y) 2.…
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Hi Guillaume, Ah, but nonparametrics is a little more tricky with Bayes, as it inverts a generative model. The rank transform will produce OK result but only with large N (from a Bayesian perspective). Anyway, perhaps this is of interest: http://ge…
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I'll ask Johnny what's up. I think the issue came up before. Do you have a concrete example? Cheers, E.J.
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I think we might just add your ANOVA inclusion idea to JASP. Let me discuss this with the team. It is just another re-organization of the massive model table, with probability zero assigned to a specific subset of models. E.J.
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Hi Sebastiaan, Yes, your understanding is correct. I think that the key aspect where your Baws Factor deviates from the inclusion BF is in the selection of the relevant models. For judging a main effect A, you disregard the models of higher complexi…
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I just implemented the inclusion BF as one would for regression. For the case of ANOVA it is perhaps worthwhile to consider more carefully the kind of comparisons one would like to make. Different inclusion options could be made available in JASP. W…
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OK, I have briefly considered the Baws factor and I think it makes sense! However, I'd compute the result slightly differently. The problem is that you have to be careful about averaging BFs (e.g., the average of 1/3 and 3 is not 1). So suppose you …
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OK I will address your comments one at a time. First off, yes, the inclusion BF need not give the same result as the direct model comparison. The direct comparison focuses on two models, and the inclusion BF averages across all of them. You state: …
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About the strong evidence for the main effects. A more informed assessment requires a look at the Bayes factors for the individual models. However, the Bayesian approach here is different from the classical approach, as it considers all of the possi…
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A few quick responses: 1. Consider your very first example. The evidence for including the interaction (over the two main effects model) is .448/.066 = 6.79. I am not sure whether this will be exactly the same as the value from your recoded "va…
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Well, this is interesting. Let me start by stressing that this is the result that probability theory gives us. So given the prior and the likelihood, these are the unique results. Our job here is to understand why it all makes sense (because probabi…
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Hi Jeff, Right now, JASP does not offer mediation and moderation analysis. It is on the agenda of course. Cheers, E.J.
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Ah, yes, well spotted. We'll take it into consideration! As an aside, the examples will be expanded and organized better in one of the next versions. E.J.
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Yes, that's correct.
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Hi peterose1, JASP does not allow "pure" interaction effects (meaning without the constituent main effects). This is the principle of marginality, discussed in part II available at https://osf.io/ahhdr/ Cheers, E.J.
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Well I am of two minds here. On the one hand I want those Bayesian assumption tests. On the other hand, the purpose of an assumption test is not to test the presence of a violation -- rather, you want to estimate its impact. So an estimation approac…
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Hi Niklas, Re. (1): Yes. Re. (2): Good points. No the best model is not convincing. And I am not sure that it is correct to assess the three-way interaction by comparing the most complex model to the model that lacks the three-way interaction (beca…
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Hi Anna, Thanks for sending the file. What has happened is that you conducted a one-sided analysis for the July test, probably by mistake. The first one made sense (Hornsund > Edinburgh), but the second one (Edinburgh > Hornsund) is so strong…
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Hi Anna, I think you have to make this public, or else invite me (EJ.Wagenmakers@gmail.com) Cheers, E.J.