EJ
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- EJ
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Comments
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Not yet, but the upcoming version (expected this month) will have a first implementation of this. E.J.
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That should be mentioned in the documentation. And you should be able to change this in the GUI...anyway, a check is to use a one-factor RM ANOVA with two levels and see whether you get the same result. E.J.
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Sorry to have missed this earlier. The post was repeated later and I've answered it there. E.J.
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Hi MikJ, Thanks for sticking around, and sorry I missed this. Will bring it to the attention of the group. Some quick comments: The table is probably easier to interpret if you put "best model on top". It may be even easier to interpret if…
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Hi Ravi, I don't think we have this yet, but I'll ask. Cheers, E.J.
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I'll pass this on to our experts Cheers, E.J.
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I'll ask our expert! Cheers, E.J.
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At the moment the Bayesian mixed model functionality does only parameter estimation, not null hypothesis testing Cheers, E.J.
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Hi Marijn, I'll ask our expert. This seems to be information that ought to be reported in the help file... Cheers, E.J.
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Hi MB, We took our inspiration from the Hastie & Tibshirani book. So I would basically consult a classic text (or watch some tutorial videos on YouTube) Cheers, E.J.
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This is really an issue for our GitHub page, but I'll direct out programmers here. My initial guess is that this is due to the admin settings. If you can't change these, what you can do is to download and unzip the pre-installed version of JASP. E.J.
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Dear num3, This is not trivial. I believe the Rouder et al. 2009 PBR paper provides some details, and so does the Gronau et al. 2020 American Statistician paper " Informed Bayesian t-tests." (available on my website). And then there are th…
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I'll pass this on to our experts E.J.
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Not right now, but it would be a good feature request for our GitHub page! E.J.
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Hi Max, Johnny van Doorn's work is relevant here (it is the basis of the implementation in JASP). The help file gives van Doorn, J., Ly, A., Marsman, M., & Wagenmakerss, E.-J. (2020). Bayesian rank-based hypothesis testing for the rank sum test,…
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We have worked hard on this, and I am happy to report that the next version of JASP will have R code integration. We hope to have a "nightly" (experimental version) with this functionality out any day now. For this particular version, the …
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Dear B_S, I suspect the computation of the CI interval for Spearman's rho requires the use of the bootstrap. Since the bootstrap is ticked off by default, no intervals are initially produced for Spearman's rho. It would be possible to have the boots…
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Thanks Quentin! E.J.
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With your sample size and design, I am not surprised that p=.041 maps on to BF01 = 10/6. I'll ask other team members about this and see what they say. Cheers, E.J.
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This is a rundimentary editor. Feel free to post suggestions for enhancement on our GirHub page. You can set the scale on each axis separately. E.J.
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That's great -- feel free to elaborate how you solved it, to help other users! I am sorry I did not get to address this issue straight away.
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I'll ask our expert! E.J.
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Dear C, You are interpreting the output correctly. The discrepancy to the frequentist result worries me a little and I'd like to get to the bottom of it. Did you use our latest ANOVA implementation? (see https://jasp-stats.org/2022/07/29/bayesian-re…
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Dear C, >Can I also rely on the Bayesian ANOVA results if my data is ordinal? The same issues apply as for frequentist inference. >I have my DV labelled as ordinal in JASP - am I right that this is not considered when I perform a Bayesian ANOV…
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Probably. I'll make sure we add this information to the help files. E.J.
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Hello Julia, Yes you've understood that correctly. Yes it would make sense to set that to the same value (I'd use the scale for the continuous covariates) If you include terms in the null model, these terms are common to the null and the alternative…
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I'd go over https://psyarxiv.com/s56mk Ultimately this is about the prior plausibility of the hypotheses. E.J.
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Ah I see. I am sure it is the non-parametric bootstrap. It seems to be you can bootstrap just about anything. If you want to check you could set the number of bootstrap samples to something really low (e.g., 10) and see whether the results change wh…
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Where exactly do you see the bootstrap option? And this is not clarified in the help file? E.J.
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well, it is always more elegant to account for the entire hierarchical structure instead of averaging, but averaging does give you some robustness, so I'm fine with averaging here