similar to: New book: Beginner's Guide to GAM with R

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 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

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):

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,

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:

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: 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

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 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 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]]

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 R Community, I?m relatively new in the field of R and I hope someone of you can help me to solve my nerv-racking problem. For my Master thesis I collected some behavioral data of fish using acoustic telemetry. The aim of the study is to compare two different groups of fish (coded as 0 and 1 which should be the dependent variable) based on their swimming activity, habitat choice, etc.

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

Dear All, the lm() function has the possibility to create a subset of the possible explaining variables that you have. However, in the help there is no example how to use this subset option. I tried the following: model<-lm(dependent.data$MPFD~.,data=explaining.data,subset=c(1,0,0,0,0,0,0,0,1,1,0,0)) MPFD is the dependent variable stored in the data frame dependent.data, and all 12

Hi, I am trying to add multiple variance structures such as the first example below: vf1 <- varComb(varIdent(form = ~1|Sex), varPower()) However my code below will not work can anybody please advise me? VFcomb<-varComb(varExp(form=~depcptwithextybf),varFixed(form=~FebNAO)) also if you have two variables with the same weights function would you write that as:

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

Hi, All, I have a dataset with binary response ( 0 and 1) and some numerical covariates. I know I can use logistic regression to fit the data. But I want to consider more locally. So I am wondering how can I fit the data with 'loess' function in R? And what will be the response: 0/1 or the probability in either group like in logistic regression? Thank you, Cindy [[alternative HTML

Hi - I read through dse package manual a bit. I'm not quite certain how I can use it to estimate a time varying coefficient regression model? I might pick up an inappropriate package. Any suggestion would be greatly appreciated. Thank you. rh