EJ
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- EJ
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Dear Ester, Perhaps you only have fixed effects, in which case I'd just report those. The ANOVA priors were proposed by analogy to the t-test; if you conduct a between-subjects t-test with the default r=.707 setting you ought to get the same result…
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Hi Kazimiera_Worf, Yes, absolutely! Cheers, E.J.
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Dear Mvs, Depends on what you mean with "hierarchical regression". Usually this means that you can add predictors in batches, and assess whether the new batch of predictors ought to be included. And yes, our linear regression functionalit…
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Yes, that's correct. E.J.
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Hmm I'm not sure. Before I attend the author of this module to your question...have you seen the JASP blog post on this module? https://jasp-stats.org/2017/11/15/meta-analysis-jasp/ E.J.
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Re effect size: this is not straightforward (I think). There's a paper with Maarten Marsman that is currently somewhere in the review system. We need to polish the Bayesian ANOVA anyway in order to show parameter estimates. We'll take the effect siz…
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Hi Tom, The Bayesian ANOVA (it is really a linear mixed model, see the BayesFactor documentation) makes the same assumptions as the classical ANOVA. We just have not developed the Bayesian echoes for those assumption tests (yet). We will do this in…
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Hi Nils, Yes, your interpretation is correct. The analysis of effects model-averages across a range of models. You could also look at the standard table and compare the the full model with the model that includes only the two-way interactions. Plot…
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Yes, but BF10 = .2 is more difficult to interpret than the mathematically equivalent statement BF01 = 5. you can report that there is evidence for H0, but the table indicates how much -- it matters whether BF01 = 1.5 or 8. If you want to summarize t…
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Hi Alexa, For DV2, you see that all BF10s < 1. This means that you have evidence for H0 -- if you set the BF display option to "BF01" instead of the default "BF10" you see how much more likely the data are under H0 than H1 un…
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Hi Rob, Thanks for the feedback and the nice words. If you like your suggestion to have permanent impact you can post it on our GitHub page (https://jasp-stats.org/2018/03/29/request-feature-report-bug-jasp/). I am of two minds about adding the nor…
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Hi Mila, Yes. The correct conclusion is that the data do not provide information: the hypotheses under consideration predicted the data about equally well. Of course, the data may be informative in other ways; for instance, perhaps the posterior di…
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Hi Mila, BF10 = 1/BF01, so if BF10 < 1 then BF01 > 1. In other words, it is impossible that both BF10 and BF01 are lower than 1, unless you are referring to different tests. Maybe you have a concrete example? In general, BFs near 1 are not di…
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Dear bmc0012, I assume you mean a hierarchical design with multiple observations per unit (?). For linear regression, JASP implements the BAS package, which does not deal with this situation. Maybe the BayesFactor package can do this -- you could c…
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I checked. The numerical value is correct, so the log(BF) = 23, but in the Jeffreys categorization scheme this is of course "extreme" evidence (we have to take the exponent) E.J.
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No, but it would be a good feature request. I've added it for you on GitHub (https://jasp-stats.org/2018/03/29/request-feature-report-bug-jasp/) E.J.
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https://link.springer.com/article/10.3758/s13428-016-0739-8 Jamil, T., Ly, A., Morey, R. D., Love, J., Marsman, M., & Wagenmakers, E.-J. (2017). Default "Gunel and Dickey" Bayes factors for contingency tables. Behavior Research Methods…
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Dear dsidhu, * I recall that Richard explicitly discusses the case of categorical predictors mixed with continuous predictors...ah yes, here: http://bayesfactorpcl.r-forge.r-project.org/#glm You could consult the documentation for further details, …
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Yes you can still compute the BF, that is, assess the relative predictive adequacy of the hypotheses. But what it means when the model is wildly misspecified is anybody's guess. See for instance http://www.thefunctionalart.com/2016/08/download-datas…
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Dear JASPUser, I would have to ask the team member responsible for this, or you could check our code, but it seems to me that "t.test" in R uses the Welch test by default ("var.equal = FALSE"). You could check their documentatio…
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Hi mspr, * The BF10 =1 because the "H0" in the comparison is the model on the first row -- in other words, the model is compared against itself. * We have some post-hoc correction for pairwise comparisons in the ANOVA. It is based on an a…
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If I'm not mistaken this has already been implemented in the developer's version! Cheers, E.J.
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Dear Alekhya, Thanks for your question. This is tricky! Fundamentally, there is nothing holding you back from using the regular model and denoting the missing conditions as missing data. But what to do in your specific case? Basically, my advice is…
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Dear mspr, Yes this is possible. If I recall correctly, the Jamil paper also discussed a data set on professions held by fathers and their sons, and that was a pretty large table. Cheers, E.J.
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We are working on integrating JASP completely with R, but it may take a while.
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Hi Jan, I'll forward this question to our logistic regression expert, but I think the expert will probably want to know what you mean exactly. In logistic regression, your dependent variable needs to be binary; for the predictors there is no such r…
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Hi Elien, This topic occasionally pops up on this forum, so searching for the relevant terms will bring up some relevant posts. As you suggest, the BF inclusion is the change from prior to posterior odds, where the odds concern all the models with …
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Dear soulclimber, So Attention is a single-number, between-subjects variable? What people sometimes to is discretize the Attention variable so that they can look for an interaction in an ANOVA. Clearly this discretization is somewhat arbitrary and …
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Dear powg, The standard output allows you to compare a main-effects only model to a model with the interaction included (so only two models are involved). In the analysis of effects, the inclusion BF compares all models with a particular term to al…
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The Tversky & Kahneman formulation expresses the odds, not the probability. If you write that equation out in full (including the = sign) you will see that the posterior odds is 0.7058824. To get to the associated posterior probability you need …