alonzivony
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Update: I think I solved the problem. As far as I can see, the issue was likelihood ratio test vs. Wald test. glmer can't abide with likelihood ratio test, but other packages can, like simr: doTest(model, fixed("IV", "lr")). Th…
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okay, my current guess is that the difference comes from JASP evaluating the model using likelihood ratio tests and R uses Wald tests. For my purposes, this is a big problem, because I only turned to R because I wanted to use a power simulation. BU…
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for the chi-square I used: car::Anova(model, type = 3, test.statistic="Chisq") I think that JASP is calculating the overall model differently because the fixed effects in JASP produce the same p-values as the fixed effects in R, but not th…
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update: a good soul in "R Users Psychology" facebook group helped me out. The answer is: lmBF(RT ~ IV1 + IV2 + Subject + IV1:Subject, whichRandom=c('Subject','IV1:Subject),data = RTData)
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Thank you both!
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Thank you E.J. and Quentin
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Oh, I don't want to be a bother. I understand now (at least partially) that its not the number of subjects or observations that made the difference. I think I understood the difference between my results, and it simply comes down to error rates. Be…
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Hi EJ. Sorry to bother. Any insight from Richard Morey on the case?
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Thanks, that will be greatly appreciated. I can also post a sample of the data if it would help.
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Thanks E.J. When I conduct frequentist analysis, repeated measures on means and mixed models with subject intercept as a random factor usually give very similar results. But I found this large discrepancy between Bayesian analysis on means and mixed…