Power analysis for mixed-effects model
- Thread starter LucieR
- Start date
It could be done in any reasonable programming language. R would be the easiest though. Basically you just need to fully write out what the data generation process looks like under your proposed alternative hypothesis and then use built in random number generators to simulate the values from those distributions and then do the analysis. One nice benefit of this approach is that the code for analyzing the data is already created after this point.
If you have specific questions we'd be more than happy to help but without specific details that's about all I can say.
If you have specific questions we'd be more than happy to help but without specific details that's about all I can say.
Unfortunately, I don't really have any experience in R/programming in general (beyond the basics). I ran the mixed-effects models in Jamovi - they included one continuous predictor, one categorical predictor, and two cluster variables (two random intercepts). I don't suppose there is any other way this can be done?
Thank you, I will have a look into that!
Could this be an alternative option - https://jakewestfall.shinyapps.io/two_factor_power/? The only issue is that you need the variance estimates to calculate the VPC which I can only get post-hoc. Is there any way around this so that I can determine the appropriate sample size using this app beforehand?
Could this be an alternative option - https://jakewestfall.shinyapps.io/two_factor_power/? The only issue is that you need the variance estimates to calculate the VPC which I can only get post-hoc. Is there any way around this so that I can determine the appropriate sample size using this app beforehand?
What wretched soul has summoned me to the board?
Yes, all my mixed model power apps are for all categorical predictors only. Not only that, but they are for balanced, orthogonal designs. Those are the only special cases in which the math works out simply enough that you can get easy analytic power calculations. If you're dealing with continuous predictors and especially correlated continuous predictors, then I recommend simulation.
Yes, all my mixed model power apps are for all categorical predictors only. Not only that, but they are for balanced, orthogonal designs. Those are the only special cases in which the math works out simply enough that you can get easy analytic power calculations. If you're dealing with continuous predictors and especially correlated continuous predictors, then I recommend simulation.
With that said it might be interesting to see how different the results are between the full simulation compared to splitting the continuous predictor into a binary variable at least for the case of power or sample size estimation.
I'm literally just spitballling and do think pure simulation is the ideal approach though.
I'm literally just spitballling and do think pure simulation is the ideal approach though.
I've recommended that to a person or two in the past. Although the app linked in this thread deals with binary predictors, my PANGEA app supports categorical variables with up to like 12 levels or something, as well as arbitrary contrasts. So my thinking has been that if you specify a 12-level categorical predictor and then compute the power for a linear contrast, it's probably a pretty reasonable approximation to the continuous case. A long time ago I wanted to investigate this approximation more closely, but I never got around to it, and now I don't really care anymore.
That approximation strategy is just for the case of a single predictor. Like I hinted earlier, things are much more difficult with multiple predictors, especially if they are correlated, for reasons I won't list here but which I'm sure you can have fun imagining.
That approximation strategy is just for the case of a single predictor. Like I hinted earlier, things are much more difficult with multiple predictors, especially if they are correlated, for reasons I won't list here but which I'm sure you can have fun imagining.