---------------------------------------------------------------------- Message: 1 Date: Tue, 11 Jun 2013 09:02:22 -0300 From: Rodrigo Tardin <rhtardin at gmail.com> To: Gavin Simpson <gavin.simpson at ucl.ac.uk> Cc: r-sig-mixed-models at r-project.org Subject: Re: [R-sig-ME] Confidence intervals in GAMM4 Message-ID: <CAE5HZZKkEmyCYre6NgJb3fciLX5pA8ZwkhaAuaXTR4=htY2ZWw at mail.gmail.com> Content-Type: text/plain Hi Gavin and other member of the list Thanks a lot for your response. You are right about the correlation structure. I was not aware, thank you. One other question that may look like very basic: Without the correlation structure (that as you said, does not exist in GAMM4 or lme4) in the mixed model, does it account for autocorrelation or no, without any specification of correlation structure it does not account for autcorrelation in the residuals. Because my data do have a problem of autocorrelation on the residuals.
Hello, Here is an alternative (and probably the only) approach: 1. Write your smoother as X * beta + Z * b (e.g. using an O'Sullivan spline) 2. Add more covariates to your predictor function (if needed) 3. Add random effects to the predictor function, and also an auto-regressive correlation structure on the residuals in the predictor function. 4. Put the whole thing in JAGS and let it run for a while Plenty of papers are available for step 1...see for example: ON SEMIPARAMETRIC REGRESSION WITH O?SULLIVAN PENALIZED SPLINES M. P. WAND AND J. T. ORMEROD They also provide R code for such smoothers. No need to dive into the underlying stats. Steps 3 & 4 are described in our upcoming book 'Beginner's Guide to GLM & GLMM with R" Zuur, Hilbe, Ieno available next week Other smoother options are described in 'A Beginner's Guide to GAM', Zuur (2012) Or in Wood (2006), or Ruppert et al. (2003). Plus a whole bunch of papers from Wand. Alain
Thanks in advance Rodrigo
Dr. Alain F. Zuur First author of: 1. Analysing Ecological Data (2007). Zuur, AF, Ieno, EN and Smith, GM. Springer. 680 p. URL: www.springer.com/0-387-45967-7 2. Mixed effects models and extensions in ecology with R. (2009). Zuur, AF, Ieno, EN, Walker, N, Saveliev, AA, and Smith, GM. Springer. http://www.springer.com/life+sci/ecology/book/978-0-387-87457-9 3. A Beginner's Guide to R (2009). Zuur, AF, Ieno, EN, Meesters, EHWG. Springer http://www.springer.com/statistics/computational/book/978-0-387-93836-3 4. Zero Inflated Models and Generalized Linear Mixed Models with R. (2012) Zuur, Saveliev, Ieno. http://www.highstat.com/book4.htm Other books: http://www.highstat.com/books.htm Statistical consultancy, courses, data analysis and software Highland Statistics Ltd. 6 Laverock road UK - AB41 6FN Newburgh Tel: 0044 1358 788177 Email: highstat at highstat.com URL: www.highstat.com URL: www.brodgar.com