Any ecologist dealing with mixed-effects models and model selection philosophy - I can't stress enough how fucking great this paper is. I keep coming back to this time and time again. Core reading for any level of science.
https://t.co/Wpq1rcnexg
@WeAreRLadies I really like this article https://t.co/pxNge4ZwTB published in @thePeerJ it's a general introduction on mixed-modelling that point to many other resources to go deeper in different areas (fixed vs. random effect, model selection, generalized or not?)
@WeAreRLadies Tutorials by Murray Logan (https://t.co/kYyyifvNQ5), bestNormalize package (https://t.co/m7WLSQTw7C), performance package (https://t.co/B7RO1mMef4), R Markdown in general
(https://t.co/c5kTH2qlM4), Harrison et al. PeerJ paper (https://t.co/mTXZMkJpZD)
@WeAreRLadies I've been finding Harrison et al's "A brief introduction to mixed effects modelling and multi-model inference in ecology" super helpful working out suite of model fit diagnostics --> Introductory ecological statistics https://t.co/JhE6rpOamX via @thePeerJ
Interesting paper @LoicQuevarec @clement_car @t_guillerminet :
A brief introduction to mixed effects modelling and multi-model inference in ecology https://t.co/aAF4aXG8DJ via @thePeerJ
Such an insightful educating paper on multi-model inference in #ecology:
Harrison et al. 2018. A brief introduction to mixed effects modelling and multi-model inference in ecology
https://t.co/OWZ90TpfhH
#AcademicChatter #statistics #phdchat