František
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 František
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Hi Katie, The resulting effect size measure is solely determined by the input. In other words, if you use Cohen's d as the input, the output is going to be on Cohen's d again. If you use correlations as the input, the output is a correlation (I woul…

Hi Howard, Yes, I can see that as a useful feature indeed (so far, you can obtain the OR by exponentiating the nonresponse scale output manuallyin case someone else comes to this thread later). Thanks for looking into this with me, Frantisek

Hi Howard, Thanks for catching that outI haven't used emmeans for quite a bit and didn't know about this. I finally had a bit more time to take a deeper look. Using the following code, it seems like that using regrid (as we do in JASP) helps with …

Hi Andrew, You can get OR from the contrasts only if you don't use the "Outcome scale" option. Otherwise, the contrast is a difference in the probabilities of those two outcomes. Glad I could help, Frantisek

Hi Howards, Hmm, that's quite interesting that R produces the output on OR scale (I would expect it to use the specified contrasts by emmeans directly). Also, in JASP we use a combination of the two functions too, i.e., https://github.com/jaspstats…

Just to check, did you use the 'emmeans::emmeans' function to create the marginal means object and then the 'emmeans::contrast' function to compute the contrasts? We did explicitly separate the calculations in the underlying implementation to always…

Dear Andrew, I'm slightly confused about your question now. When you have a factor with two levels, the sum contrast creates (1, 1) coding for the levels (i.e., contr.sum(2)). In other words, the fixed effect estimate corresponds to half the differ…

Hi Howard, Thanks for posting the question. I think that the main difference is that you do not compute the contrast on the outcome scale (probabilities) but rather on the latent model scale and then transform it into odds ratios. I.e., under the sp…

Hi Andrew, The contrast coding can be indeed a bit confusing. We added a more detailed explanation to the help file that summarizes what are the differences and reasons for using the sum contrasts (where the fixed effects are not readily interpretab…

Hi Jonathan, The prior distributions are weakly informative and should be wellbehaved in parameter estimation settings. The module uses the default prior distribution settings of the rstanarm R package which defines normal(location = 0, scale = 2.5…

This seems to be an issue with the maximum length of R formula  it cannot be longer than 500 characters. In such a case, I would recommend shortening variable names (e.g., Emotion > Em, Stimulus orientation > StOr, etc.). In case that does…

We resolved this via email with the help of Thomas Debray. I'm attaching the response if other people have a similar question: The I2 statistics you report are OK, they usually represent the I2 of the logit transformed cstatistic (unless no transf…

Hi Peter, Yes, EJ is indeed right. If you input the Bayesian posterior estimates, you are adding additional information to each study via each ttest's prior distribution. The `frequentist ttest's` Cohen's d is just a summary statistic used as an …

Hi Fara, Yes, absolutely! (also Fisher's z is usually the best standardized effect size for performing metaanalyses) Cheers, Frantisek

Hi Stan, Thanks for reaching out! Yes, you are correct about the issues with depend effect sizes. We currently do not have such procedures in JASP but we are working on adding them in the future. Best, Frantisek

Hi Daiichiro, Yes, that's exactly the way how the contrasts are constructed. Regarding the plot, the visualized points are the raw data aggregated across the random effects specified in "Background data show" field, e.g., participants in …

Hi Daiichiro, Thanks for reporting the issue. Regarding Q1, the fixed effects estimate summary table shows estimates for the sum coded contrasts  those are usually not directly interpretable (in comparison to dummy coding) but present a better way…

Hi, I'm sorry, I'm not really familiar with this type of multilevel model, but I fear that it is not possible in the current JASP implementation. Cheers, Frantisek

Hi JP, You can definitely plot levels of one variable against the levels of the other variable  the interaction then looks like a difference in the changes among the levels. Or do you have something else in mind? (And If you could upload an exampl…

Hi KC, Yes, the Bayesian (Generalized) Mixed Models analysis uses the autoscaled priors from rstanarm (as the analysis provides estimation only and no Bayes factors). We should've described this better in the documentation  we are planning to make…

Hi Marijn, Yes, the Bayesian (Generalized) Mixed Models analysis is using the autoscaled priors from rstanarm (as the analysis provides estimation only and no Bayes factors). We should've described this better in the documentation  we are planing …

Hi Smiddy, We actually started drafting the paper in December, so we will hopefully have preprint out soon :) Cheers, Frantisek

Hi Tom, We are currently preparing an article that will provide more guidance on operating the Mixed Effect module and we will definitely include a section about the contrasts settings. The latest release also includes a new example analysis, "…

Yes, that's indeed the case. Frantisek

Hi horotat, Estimating complex GLMMs can indeed be problem. Unfortuntelly, we did not implemented the optimizer control in the JASP interface yet (I just created a feature request: https://github.com/jaspstats/jaspissues/issues/1832 so we might ad…

Hi ataylor, Could you please save and send me the analysis file which leads to the error (as .JASP)? I can take a look and check what is wrong. Cheers, František (f.bartos96@gmail.com)

Hi Satoru, the JASP implementation of GUI does not currently offer interaction of random grouping factors right now subject:music . For now, you can sidestep this issue by creating a compound variable "subject_music" that just contains a …

Dear dmalm, As EJ noted, a Bayesian MANOVA would be the most appropriate analysis given your description. Unfortunately, the Mixed Models module does not provide multivariate models (and I haven't seen them in any application outside of textbooks ye…

Hi Daniel, Glad you like the interface. Yes, I create a feature request on the JASP GitHub page on your behalf: https://github.com/jaspstats/jaspissues/issues/1628. Cheers, Frantisek