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Feb 16, 2020 at 21:15 history edited Matt Barstead CC BY-SA 4.0
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Dec 19, 2018 at 14:17 vote accept Eric Lino
Dec 10, 2018 at 21:41 comment added Matt Barstead You may want to check out the simR package. To my knowledge it is the most flexible existing package for power analyses with linear and generalized linear models. Green, P., & MacLeod, C. J. (2016). SIMR: An R package for power analysis of generalized linear models by simulation. Methods in Ecology and Evolution.
Dec 10, 2018 at 21:27 comment added Eric Lino I see. Well, I'm using the glmmadmb package, developed by Ben Bolker & others. My response variable is zero inflated (number of people with a specific rare disease) and my independent variables include normal, non-normal and zero inflated distributions. Since I'm dealing with a time series, I used "year" as a grouping factor and it seemed like a good idea to explore the ZIGLMM family of models. Does this information help you in helping me?
Dec 10, 2018 at 11:37 comment added Matt Barstead For R packages and power it depends on your model (pwr, simsem, etc). There is not a single answer. Also in terms of the odds of subsetting your data I think that is just another way of asking about power if I understand you correctly. If you are gravitating toward the power bit I would recommend concentrating on your weakest effect and seeing what the minimal sample size would have to be to replicate it - a sort of worst case scenario.
Dec 10, 2018 at 3:33 comment added Eric Lino I appreciate all the time you spent writing such a in-depth answer, Matt. However, I feel that although it helps me in a conceptual level, it lacks some reference that I will very much need to discuss this approach with my supervisor. Would you happen to have any papers/books on the odds of subsetting data? If not possible, would you recommend a R package on which i can perform this power analysis you spoke of?
Dec 9, 2018 at 1:50 history edited Matt Barstead CC BY-SA 4.0
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Dec 9, 2018 at 1:39 history edited Matt Barstead CC BY-SA 4.0
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Dec 9, 2018 at 1:33 history answered Matt Barstead CC BY-SA 4.0