similar to: Intro GAM and GAMM course: Singapore

We are pleased to announce the following book: Spatial, Temporal and Spatial-Temporal Ecological Data Analysis with R-INLA Authors: Zuur, Ieno, Saveliev Book website: www.highstat.com Paperback or EBook can be order (exclusively) from www.highstat.com TOC: http://highstat.com/Books/BGS/SpatialTemp/Zuuretal2017_TOCOnline.pdf Summary: We explain how to apply linear regression models,

We would like to announce the following statistics course: Course: Introduction to Linear Mixed Effects Models and GLMM with R Where:? Lisbon, Portugal When:?? 19-23 February 2018 Course website: http://highstat.com/index.php/courses Course flyer: http://highstat.com/Courses/Flyers/2018/Flyer2018_02LisbonV2.pdf Kind regards, Alain Zuur -- Dr. Alain F. Zuur Highland Statistics Ltd. 9 St

We are pleased to announce the following book: Title: Beginner's Guide to Zero-Inflated Models with R Authors: Zuur, Ieno Book website: http://www.highstat.com/BGZIM.htm Paperback or EBook can be order (exclusively) from: http://www.highstat.com/bookorder.htm TOC: http://www.highstat.com/BGS/ZIM/pdfs/TOCOnly.pdf Keywords: 430 pages. Zero inflated count data. Zero inflated continuous data.

We would like to announce the following statistics course: Introduction to zero inflated models and GLMM 13 - 16 May 2013. Elche, Spain. For details, see: http://www.highstat.com/statscourse.htm Course flyer: http://www.highstat.com/Courses/Flyer2013_05Elche_ZIP.pdf Kind regards, Alain Zuur -- Dr. Alain F. Zuur First author of: 1. Analysing Ecological Data (2007). Zuur, AF, Ieno, EN and

There are 8 remaining seats on the following course: Introduction to linear mixed effects modelling and GLMM course. Where: Banff, Canada When: 27-31 May, 2013 For details see the flyer at: http://www.highstat.com/Courses/FlyerCanada2013.pdf or: http://www.highstat.com/statscourse.htm Kind regards, Alain Zuur -- Dr. Alain F. Zuur First author of: 1. Analysing Ecological Data (2007).

We would like to announce the following statistics course: Data exploration, regression, GLM & GAM. With introduction to R When: 4 - 8 February 2013. Where: Coimbra, Portugal. For details, see: http://www.highstat.com/statscourse.htm Course flyer: http://www.highstat.com/Courses/Flyer2013FebCoimbra.pdf Kind regards, Alain Zuur -- Dr. Alain F. Zuur First author of: 1. Analysing

We would like to announce the following statistics course; Course: Introduction to Linear mixed effects models, GLMM and MCMC with R When: 10-14 February, 2014 Where: Pousada de juventude parque das nacoes. Rua de Moscavide, Lt 47 ? 101, 1998- 011. Lisbon, Portugal Info: http://www.highstat.com/statscourse.htm Flyer: http://www.highstat.com/Courses/Flyer2014_02SIM_LisbonV2.pdf Kind regards,

Apologies for cross-posting. We would like to announce a 3-day R course in Lisbon, Portugal. Full details can be found at: http://www.brodgar.com/statscourse.htm Kind regards, Alain Zuur Dr. Alain F. Zuur First author of: Analysing Ecological Data (2007). Zuur, AF, Ieno, EN and Smith, GM. Springer. 680 p. URL: www.springer.com/0-387-45967-7 Other books: http://www.brodgar.com/books.htm

Simplify your model. Does your TandemRepeat have a lot of levels? Or is your sample size very small? Alain Dear all, I am fitting a hurdle model in the following way: HNB <- hurdle(chro ~ as.factor(TandemRepeat)| as.factor(TandemRepeat), data =data_negbin_fin, dist = "negbin") But the std. error for log(theta) = NA Count model coefficients (truncated negbin with log link):

Members of this mailing list may be interested in the following new book: Beginner's Guide to GLM and GLMM with R. - A frequentist and Bayesian perspective for ecologists - Zuur AF, Hilbe JM and Ieno EN This book is only available from: http://www.highstat.com/BGGLM.htm This book presents Generalized Linear Models (GLM) and Generalized Linear Mixed Models (GLMM) based on both

R users doing data analysis may be interested in the following paper: http://methodsblog.wordpress.com/2009/11/13/first-paper-now-online/?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+wordpress%2Fmethodsblog+(methods.blog) All data and R code is available. Alain ----- -------------------------------------------------------------------- Dr. Alain F. Zuur First author of:

Following our successful book: 'A Beginner's Guide to R', we are please to announce an online R course based on this book. Video/audio files Discussion board Video footage of instructors Nearly every section of ?A Beginner?s Guide to R? is covered. Each chapter is presented as a powerpoint video file with audio comments and you can see our computer screen with R code and results.

There are a few places left on the following course: Beginner's Guide to MCMC, GLM and GAM with R When: 10 - 13 June 2013 Where: SAMS, Oban, Scotland Further information: http://www.highstat.com/statscourse.htm Flyer: http://www.highstat.com/Courses/Flyer2013June_SAMS.pdf Kind regards, Alain Zuur

Readers of this mailing list may be interested to know that the book "A Beginner's Guide to Generalized Additive Models with R' is now available from: http://www.highstat.com/BGGAM.htm Upcoming books in 2013: A Beginner's Guide to GLM with R and JAGS. AF Zuur, J Hilbe, EN Ieno A Beginner's Guide to GAMM with R. AF Zuur, AA Saveliev, EN Ieno Kind regards, Alain Zuur

Hi all- I fit a zero-inflated Poisson model to model bycatch rates using an offset term for effort. I need to apply the fitted model to a datasets of varying levels of effort to predict the associated levels of bycatch. I am seeking assistance as to the correct way to code this. Thanks in advance! Laura [[alternative HTML version deleted]]

Dear R-users, To obtain the percentage of deviance explained when fitting a gam model using the mgcv library is straightforward: summary(object.gam) $dev.expl or alternatively, using the deviance (deviance(object.gam)) of the null and the fitted models, and then using 1 minus the quotient of deviances. However, when a gamm (generalizad aditive mixed model) is fitted, the

Hi all, I would like to fit a gamm model of the form: Y~X+X*f(z) Where f is the smooth function and With random effects on X and on the intercept. So, I try to write it like this: gam.lme<- gamm(Y~ s(z, by=X) +X, random=list(groups=pdDiag(~1+X)) ) but I get the error message : Error in MEestimate(lmeSt, grps) : Singularity in backsolve at level 0, block 1

I am having difficulty fitting a mgcv::gamm model that includes both a random smooth term (i.e. 'fs' smooth) and autoregressive errors. Standard smooth terms with a factor interaction using the 'by=' option work fine. Both on my actual data and a toy example (below) I am getting the same error so am inclined to wonder if this is either a bug or a model that gamm is simply unable

Try to fit a longitudinal dataset using generalized mixed effects models via the R function gamm() as follows: library(mgcv) gamm0.fit<- gamm(y ~ x+s(z,bs="cr"), random=list( x=~1, s(z,bs="cr")=~1 ), family = binomial, data =raw) the data is