Thank you so much for this video!!! I wanted to ask you if you have made a video but using glmer ( the dependent variable 0 or 1). Or if you have any recommendations for that, particularly to assess the assumptions. Thank you in advance for your kind reply!
Thank you very much for these amazing informative videos! The summary() function outputs comparisons of groups C5-8 and paraplegia with the reference group C1-4 in terms of time group interaction. How could one compare C5-8 to paraplegia in terms of interaction, both linear and quadratic? Thank you!
Hi, thank you for your video. Could you make a video of linear mixed effect with repeated measures, like in crossover designs? I really will appreciate it.
Thanks Nathaly. Please see my other video of mixed-effect models for factorial designs: ua-cam.com/video/x467LStTtHU/v-deo.html You might also be interested in this pre-print (not peer reviewed yet) on arXiv: arxiv.org/abs/2209.14349
This is a really informative and helpful video! I wondered if you could point me in the direction of resources to support an a priori power analysis for mixed effects models for longitudinal data?
Hi Gemma, that can quickly become a complicated subject! In brief though, GLIMMPSE is a web based platform that is free and there are since free packages in R (like “simR”) … and then PASS is a paid program for a lot of power calculations (including mixed models) that makes it easier but only covers limited models. I will try to do a video doing a power analysis through simulations in R soon.
I believe if the time variable is not the variable of study, we don't care that much about multicollinearity or we should at least prove that mean centering improves the VIF in order to use it. If time variable is merely a control variable, well....we don't care about multicollinearity.
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Thank you so much for this video!!! I wanted to ask you if you have made a video but using glmer ( the dependent variable 0 or 1). Or if you have any recommendations for that, particularly to assess the assumptions. Thank you in advance for your kind reply!
Thanks for sharing your lectures ❤
Thank you very much for these amazing informative videos!
The summary() function outputs comparisons of groups C5-8 and paraplegia with the reference group C1-4 in terms of time group interaction. How could one compare C5-8 to paraplegia in terms of interaction, both linear and quadratic? Thank you!
Hi, thank you for your video. Could you make a video of linear mixed effect with repeated measures, like in crossover designs? I really will appreciate it.
Thanks Nathaly. Please see my other video of mixed-effect models for factorial designs: ua-cam.com/video/x467LStTtHU/v-deo.html
You might also be interested in this pre-print (not peer reviewed yet) on arXiv: arxiv.org/abs/2209.14349
This is a really informative and helpful video! I wondered if you could point me in the direction of resources to support an a priori power analysis for mixed effects models for longitudinal data?
Hi Gemma, that can quickly become a complicated subject! In brief though, GLIMMPSE is a web based platform that is free and there are since free packages in R (like “simR”) … and then PASS is a paid program for a lot of power calculations (including mixed models) that makes it easier but only covers limited models.
I will try to do a video doing a power analysis through simulations in R soon.
Can you explain what this lmercontrol do here?
I believe if the time variable is not the variable of study, we don't care that much about multicollinearity or we should at least prove that mean centering improves the VIF in order to use it. If time variable is merely a control variable, well....we don't care about multicollinearity.