TimKDJ
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- TimKDJ
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Hi Liesbeth, It'd be very helpful if you could post this to our Github page. Doing so will make it a little easier for us to solve your problem (it saves some going back and forth, as the issue template helps us obtain very useful information). As a…
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This will be fixed in version 0.9.3. Thanks for reporting it to us!
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Hi Sarah, It's a little tricky to determine the cause based on a screenshot. Could you please share the dataset with us or save your output as a .jasp file? You can attach it here or send it to t.dejong [at] jasp-stats.org Cheers Edit: never mind…
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I completely agree that the current plot is not informative enough. The reason why we chose this setup is because the data are supplied in wide format. This means that every level of the dependent has a unique name (for the dependent). What we can…
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Glad it makes sense to you now :) I indeed made a little mistake with the copy-pasting earlier. It should be IV 2: Incl. BF = 0.017 / 0.114 = 0.149 IV 1 * IV 2: Incl. BF = 0.868 / 0.003 = 289 To demonstrate how strong the effect of rounding is…
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Omitting the prior inclusion probabilities, IV 1: in = 0.020 + 0.003 = 0.023out = 0.094 + 0.014 = 0.108Incl. BF = 0.023 / 0.108 = 0.213 IV 2: in = 0.014 + 0.003 = 0.017out = 0.094 + 0.020 = 0.114Incl. BF = 0.023 / 0.108 = 0.149 IV 1 * IV 2: in…
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Could you maybe send me the .jasp file itself, Nicolas? (t.dejong@jasp-stats.org if you want to keep it private)
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It's a little tricky to give an excerpt of the code as it uses calculated results from other functions. Let's give it a try though! :) I attached the code to this post in a .zip file. To demonstrate what the calculated results from the other functi…