This is the introduction for sample size calculations using R for generalized linear mixed models. This is part of a training module of the Biostatistics, Epidemiology, and Research Design Core (BERDC) for the DaCCoTA project.
Thank you so much for explaining everything in such a nice way! I really appreciate it. In the model of minute 8:18, lmer(Reaction ~ Days + (Days | Subject) if my understanding was correct, we can interpret the parenthesis part as "subject as a random effect within day." On the other hand, in the two models of minute 12:13, the parenthesis part is (1 | herd). I was wondering: 1) how should be interpreted this part (what is the meaning of 1)? and 2) why we put a number instead of the name of a variable? Thank you in advance for your time and consideration :)
The format (1|group) represents a random group intercept. The number 1 is sort of a placeholder. So for (1|herd), herd is the random effect with random intercept. The format (X|group) represents a random slope and random intercept, that is a random slope of X within group with correlated intercept. This is a good resource I found that explains random effects models in R: bbolker.github.io/mixedmodels-misc/glmmFAQ.html
@@daccotabiostatistics Thank you so much for your kind reply and for your time. I really appreciate it 🙏🏼 And the link that you shared is sooo good. Many thanks. Wishing you a good day :)
I don't have plans to repost without music in the short term. You can access the content in slide format using the link below: med.und.edu/research/daccota/_files/pdfs/berdc_resource_pdfs/sample_size_r_module_glmm2.pdf
Hello and thank you for the video I would like to use GLMM multinomial logistic regression mixed model for repeated data with R software, response ~ trt + period + seqTrt + (1|id) do you know a package or a function for this model thank you in advance
I have not run a multinomial logistic regression for repeated data, so I can't say for sure. I would start with trying out a simple case of multinomial logistic regression. Here is a good source from UCLA for trying that out (stats.idre.ucla.edu/r/dae/multinomial-logistic-regression/). From there, you could test if the multinom function can handle repeated data. Otherwise, I did find this post that may be useful (hlplab.wordpress.com/2009/05/07/multinomial-random-effects-models-in-r/). Hopefully those sources help.
