Binomial Temporal GAMM does not converge (R::mgcv)
First rule of statistics is start with the simplest reasonable model and then try to build something more complex if it is necessary. I would start with a standard mixed effects with random effects for intercept and slope, judging from what you have already. This can be done in lmer, you will probably need to set the nAGQ value to something greater than 1, increase it until nothing seems to change. This is due to the high variance for the intercept random effect, and is probably causing the problem with your other analysis, as it uses PQL which just won't work well in this case. Then look at residuals versus fitted values, with a lowess curve as they will be very noisy, and see if you need something more complex. If so a regression spline is easiest using ns() and a small number of knots. Ken
On 5 June 2015 at 09:05, Benjamin Kellman <bkellman at eng.ucsd.edu> wrote:
Hello R sig, I'm new to mixed effect models and I would really appreciate some help. I've posted by question on cross validated: http://stats.stackexchange.com/questions/155524/binomial-temporal-gamm-does-not-converge-rmgcv If any of you would care to weigh in, it would be greatly appreciated. Thank you -- Benjamin P. Kellman PhD Student Bioinformatics and Systems Biology UC, San Diego [[alternative HTML version deleted]]
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*Ken Beath* Lecturer Statistics Department MACQUARIE UNIVERSITY NSW 2109, Australia Phone: +61 (0)2 9850 8516 Building E4A, room 526 http://stat.mq.edu.au/our_staff/staff_-_alphabetical/staff/beath,_ken/ CRICOS Provider No 00002J This message is intended for the addressee named and may...{{dropped:9}}