Isabella R. Ghement @IsabellaGhement 1498 days ago

RT @bolkerb: @rose_odea @MethodsEcolEvol @DanielWANoble @itchyshin This looks great. Cheating a little bit, I also wanted to piggyback/remi…

Shinichi Nakagawa | 中川震一 @itchyshin 1498 days ago

RT @bolkerb: @rose_odea @MethodsEcolEvol @DanielWANoble @itchyshin This looks great. Cheating a little bit, I also wanted to piggyback/remi…

Ben Bolker @bolkerb 1498 days ago

@rose_odea @MethodsEcolEvol @DanielWANoble @itchyshin This looks great. Cheating a little bit, I also wanted to piggyback/remind people to read this next: @MPKain's master's thesis pub on simulation-based power analyses for these kinds of studies: https://t.co/FKXxNHiD4p

Noam Ross @noamross 2053 days ago

@topepos @urganmax @JezRoff @ucfagls I like this one from @MPkain, @bolkerb and M_W_McCoy on power analysis for GLMMs: https://t.co/cni6UFSMjk

Rob Clark @Robert_E_Clark 2117 days ago

Looks useful! @CrowderLabWSU @mrbrousil https://t.co/yR42sS3pfE

John Poe @DavidPoe223 2117 days ago

Here's a cool thing: A practical guide and power analysis for GLMMs: detecting among treatment variation in random effects by @bolkerb (and coauthors I can't find!) where you can do power analysis for variance components! https://t.co/XmlCd0kMId

Jonas Lang @jwblang 2127 days ago

@bmwiernik The GLMM resources are frequently better than the LMM (and it is easy to adapt for LMMs): https://t.co/l6rCkGQMj4 and https://t.co/TMBWEZJQl6

Noam Ross @noamross 2916 days ago

@drob @LucyStats @juliasilge @HannahKrimm I like this paper on it, which has some very untidy code for you to refactor: https://t.co/cni6UFBbrM

Stuart Semple @SemplePrimate 3097 days ago

Found it! Well, @P_Tkaczynski found it really (thanks Paddy!)...https://t.co/8fhIwCgXws