GAMM4: temporal autocorrelation?
Hello Ramgad82 Without providing data or/and scatterplots it is difficult to say anything sensible..but here are a few points to consider: Problem 1: Applying the acf on the residuals assume that the residuals form a single time series. I guess you have multiple time series; one per animalID? In that case make an acf for each individual residual time series. Problem 2. You are aiming for:? s(Time) + auto-correlated time These two components might fight for the same information. The solution is to fix either the df of the smoother, or the auto-correlation parameters. 3. As to your specific question...you can either try glmmTMB or R-INLA. Kind regards, Alain ------------------------------ Message: 3 Date: Wed, 14 Mar 2018 10:50:58 +0100 From: Tagmarie <Ramgad82 at gmx.net> To: r-sig-mixed-models at r-project.org Subject: [R-sig-ME] GAMM4: temporal autocorrelation? (GAM, binomial data, random effect) Message-ID: <1ce57e0a-80e1-9c81-3e97-ad34c8649b35 at gmx.net> Content-Type: text/plain; charset="utf-8"; Format="flowed" Dear all, I am not a statistican but a biologist and I have a problem that I cannot solve. I guess more people must have that problem but I didn't find a solution online. I want to to do a gam with random effects and my response variable has a binomial error structure. Reading through literature I found that gamm() doesn't perform very good with binomial error structures and gamm4 would be better. So I did a gam using gamm4. My code is like this: Mod1 <- gamm4(grooming ~s(time,bs = "cr"),random=~(1|animalID),data=mydata, family=binomial) The result looks perfect! Unfortunately, when testing for temporal autocorrelation with pacf(resid(Mod1$gam)) I do clearly have temporal autocorrelation in my data. I had also expected that because time is of course always leading to autocorrelation structures. My problem: How do I incorporate the temporal autocorrelation into my model? The usual part which works in gams (correlation=corAR1(0.71, form = ~ 1 | animalID) won't work in GAMM4 I know. I followed an example from "?magic" (after loading mgcv) but that resulted in a crap looking result. Does anyone know how to deal with that data structure? Help would be terribly much appreciated! ------------------------------ Subject: Digest Footer _______________________________________________ R-sig-mixed-models mailing list R-sig-mixed-models at r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models ------------------------------ End of R-sig-mixed-models Digest, Vol 135, Issue 17 ***************************************************
Dr. Alain F. Zuur Highland Statistics Ltd. 9 St Clair Wynd AB41 6DZ Newburgh, UK Email: highstat at highstat.com URL: www.highstat.com And: NIOZ Royal Netherlands Institute for Sea Research, Department of Coastal Systems, and Utrecht University, P.O. Box 59, 1790 AB Den Burg, Texel, The Netherlands Author of: 1. Beginner's Guide to Spatial, Temporal and Spatial-Temporal Ecological Data Analysis with R-INLA. (2017). 2. Beginner's Guide to Zero-Inflated Models with R (2016). 3. Beginner's Guide to Data Exploration and Visualisation with R (2015). 4. Beginner's Guide to GAMM with R (2014). 5. Beginner's Guide to GLM and GLMM with R (2013). 6. Beginner's Guide to GAM with R (2012). 7. Zero Inflated Models and GLMM with R (2012). 8. A Beginner's Guide to R (2009). 9. Mixed effects models and extensions in ecology with R (2009). 10. Analysing Ecological Data (2007).