similar to: R interface

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

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

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

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

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, 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,

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.

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]]

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:

Let me start with an example: par(mfrow=c(2,3)) for (i in 1:5){ x = rnorm(100) y = .5*x + rnorm(100, 0, sqrt(1-.5^2)) plot(x,y) } Note that there's five plots and six spaces for those plots via mfrow, leaving one row empty. Is there a way to have the bottom two plots centered? I think it looks weird to have them left-justified. Thanks in advance for the help! -- Dustin Fife PhD

Dear all, I'm a beginner of R and I need to carry out some three-way mixed ANOVAs. Following examples at http://personality-project.org/r/r.anova.html, I managed to get the ANOVA part, but I don't know how can I check data normality and homogeneity of variance in R (since they're the required assumptions of ANOVA analysis). Are there any special command/packages? Could anyone give me

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

There are 4 remaining seats on the following statistics course: Course: Introduction to GAM and GAMM with R When: 30 May-3 June 2016 Where: Tropical Marine Science Institute, National University of Singapore, Singapore Course website: http://highstat.com/statscourse.htm Course flyer: http://highstat.com/Courses/Flyers/Flyer2016_05Singapore.pdf -- Dr. Alain F. Zuur First author of: 1.

Hi, does anybody know of a nice test to detect trend turning points in time series? Possibly with reference? Thanks, joerg

Hello, I'm having an error when trying to fit the next GLM: >>model<-glm(response ~ CLONE_M + CLONE_F + HATCHING +(CLONE_M*CLONE_F) + (CLONE_M*HATCHING) + (CLONE_F*HATCHING) + (CLONE_M*CLONE_F*HATCHING), family=quasipoisson) >> anova(model, test="Chi") >Error in if (dispersion == 1) Inf else object$df.residual : missing value where TRUE/FALSE needed If I fit

I'd like to estimate time varying coefficients in a linear regression using a Kalman filter. Even if the Kalman Filter seems to be available in some packages I can't figure out how to use it to estimate the coefficients. Is there anyway to do that in R? Any help appreciated Thanks

Hello, I am using R to analyze a large multilevel data set, using lmer() to model my data, and using anova() to compare the fit of various models. When I run two models, the output of each model is generated correctly as far as I can tell (e.g. summary(f1) and summary(f2) for the multilevel model output look perfectly reasonable), and in this case (see below) predictor.1 explains vastly more