@stephenjwild @IsabellaGhement Fig 4 of Pedersen et al. “Hierarchical Generalized Additive Models in Ecology: An Introduction with Mgcv.” PeerJ 2019 is really useful here: https://t.co/nCZniae3Ab @ericJpedersen @noamross @ucfagls @millerdl https://t.co/ZDbVDpGEh8
I love papers like this - written in such a way to be directly useful for people to start employing these tools in their work.
https://t.co/VW1RElLu4i
@noamross et al.
@SolomonKurz Suggestion: dv ~ group + s(time, by = group) + s(time, participant, bs = "fs")
group and participant need to be coded as factors
Suggested reading: https://t.co/AJRoemPMmp (by @ericJpedersen @millerdl @ucfagls @noamross)
@IsabellaGhement isnt that similar to model "GS" in this article?
https://t.co/iEoKRMLYvQ
global smooth, with group level smooths each with the same penalty
@GuidoBiele We discuss this in the HGAM paper https://t.co/SZEBia7QYa but the problem is more your choice of the model, yours seems over specified or unidentifiable
@ucfagls @stephenjwild @PhDemetri @bolkerb Gavin, for HGAM model comparison, the paragraph in your HGAM article https://t.co/NtuencHy5R does talk about model comparison. Does the paragraph refer solely to prediction settings for that comparison (with method = "REML" to make comparisons meaningful)? https://t.co/HX5YXGs5MP
@bmwiernik You can get (and plot) the basis functions mgcv uses using basis() and the penalty matrix via penalty() and their draw() methods from my {gratia}
Since not only detection can change but also potentially density (if we have surveys of birds on the water and birds in flight as our two "platforms"), we adapt the factor-smooth approach (https://t.co/asA2WQhXhG) to account for this https://t.co/lr73CHvwNs
@andrewheiss @tjmahr Ooo I'll check this out. I came across this on a Stan message board and they draw parallels between the two, but make smooths sound like a more flexible/less group focused approach. Or at least you control "group" boundaries more directly? https://t.co/WROcriUBtQ