Discussion: Model Selection with GLMMadaptive about glmmadaptive HOT 3 CLOSED

Dear Michel, If you want to compare between models (i.e., this is what I understand with “model selection”), you can use a likelihood ratio test if the models are nested or look at the information criteria AIC and BIC if the models are not nested. Both are available in the anova() function. Best, Dimitris From: mamscience <[email protected]> Sent: Wednesday, November 4, 2020 11:15 AM To: drizopoulos/GLMMadaptive <[email protected]> Cc: Subscribed <[email protected]> Subject: [drizopoulos/GLMMadaptive] Discussion: Model Selection with GLMMadaptive (#30) Dear dr. Rizopoulos Thanks for writing GLMMadaptive. It's been intuitive to work with, even for somebody who's new to GLMM like myself. My question is regarding model selection: What approach would you advice in model selection? In your vignette Goodness of Fit you suggest using DHARMa to aid in the selection. With binomial models, only the residuals vs fitted graph is useful to assess fitness. I believe that this is hard, particularly when all models do not fit perfectly. Can you suggest more tools or approaches for selecting models? Kind regards, Michel — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub<https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fdrizopoulos%2FGLMMadaptive%2Fissues%2F30&data=04%7C01%7Cd.rizopoulos%40erasmusmc.nl%7Cdc0b61d9fcaa42688a3908d880aa7649%7C526638ba6af34b0fa532a1a511f4ac80%7C0%7C0%7C637400816899356906%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=S8FrULaawQmSgOqRXERWdsAoN6o2FhWi5tjK7p7II7o%3D&reserved=0>, or unsubscribe<https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fnotifications%2Funsubscribe-auth%2FADE7TT4SD6QBGUZCBVD5LV3SOESRLANCNFSM4TJ3JSEA&data=04%7C01%7Cd.rizopoulos%40erasmusmc.nl%7Cdc0b61d9fcaa42688a3908d880aa7649%7C526638ba6af34b0fa532a1a511f4ac80%7C0%7C0%7C637400816899366900%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=Aya5UTIdTOwKY%2Fs0AAovRCFpibjaOPrEmCH1FW9LTmI%3D&reserved=0>.