Thank you so much! I would love if you could play with the modelization more... like different variables and effects, it would be great, so we can appreciate which model is better an why,,, you are such a good teacher. Thanks again, Bye!!
Dear Madam, I have seen most of the videos and asked for your few videos of GLM and GLMM in SPSS. If you can do it when possible, that would be much appreciated.
I was re-watching the first video of this series and realized what I question does not make sense lol. But, since we are using log (u) instead of log odds for Poisson, what do we retrieve by doing exp(B-hat)?
Really good explanation for a layman to understand the concept!!
Thank for the series, was really helpful
she wrote glmm!! Very helpful. Thank You!! Inspiring!! Good person. Need to look into GPU speedup for this if possible.
this was hype. thanks
Thank you so much! I would love if you could play with the modelization more... like different variables and effects, it would be great, so we can appreciate which model is better an why,,, you are such a good teacher. Thanks again, Bye!!
love your hair color!
Thank you!!
Welcome back
Thanks. It's been awhile.
Professor, any videos on generalized linear models? Thank you very much
This would be good for GPU speedup
Dear Madam, I have seen most of the videos and asked for your few videos of GLM and GLMM in SPSS. If you can do it when possible, that would be much appreciated.
Excelent explanation. Just one question: Even if I use Poisson family can I retrieve the probabilities using exp(B-hat) / 1+exp(B-hat) ?
I was re-watching the first video of this series and realized what I question does not make sense lol. But, since we are using log (u) instead of log odds for Poisson, what do we retrieve by doing exp(B-hat)?
It is amazing to have this explanation. Thank you!
One question, is it possible in glmm to have nested random effects?
Absolutely. Just as in LMM, you can have nested and/or crossed random effects.