similar to: (ecological) model sensitivity analysis book

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,

Dear all, I have strong interest on Ecological Niche Model, which in general use a set of environmental variables (continuous, categorical etc) and Presence (or Presense/Absence) records for species. I think that "grasp" and "adehabitat" packages could help me on these tasks. My input layers are on ASC format, and the record of species is a data-frame with X, Y, name of

Hi, Anybody can suggest a good Ecological Modelling's Book. Thanks Ronaldo -- O c?u sueco. -- pal?ndromo -- |> // | \\ [***********************************] | ( ? ? ) [Ronaldo Reis J?nior ] |> V [UFV/DBA-Entomologia ] | / \ [36571-000 Vi?osa - MG ] |> /(.''`.)\ [Fone: 31-3899-2532

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

Hi, I'm trying to conduct sensitivity analysis in R using the 'sensitivity' package. Although the description of functions seem straightforward, I can?t succeed. The definition of input factors can be the problem. library(sensitivity) #A simple model with 4 input factor to test the morris function: model01=function(a1,a2,a3,a4) { Z<-numeric(10) Z[1]<-runif(1)

Hi all, This is really a stats question as much as an R question. I'm trying to do a joint scaling test (JST - see below) on some very oddly-distributed data and was wondering if anyone can suggest a good way of dealing with model violations and/or using R to evaluate how sensitive the model is to violations of the normality assumption. Here's a quick explanation of the analysis, the

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.

Dear all, I fitted a non-parametric model using GAM function in R. i.e., gam(y~s(x1)+s(x2)) #where s() is the smooth function Then I obtained the coefficients(a and b) for the non-parametric terms. i.e., y=a*s(x1)+b*s(x2) Now if I want to use this estimated model to do optimization or sensitivity analysis, I am not sure how to incorporate the smooth function since s() may not

Hi, I've added several pages of worked solutions for the book Ecological Detective by Hilborn and Mangel to the R-wiki. My hope is that this will be of use to others working through this book without access to a local expert. I am certainly not an expert, local or otherwise. I have posted solutions for chapters 3-6, which includes some really horrible direct translations of the pseudocode

Hi, I was just wondering if there are any books on R that have applications using Hidden Markov models or anyone with tutorials/exercises that can be shared. Ideally, these would have a bio/ecological slant though not neccesarily. Any suggestions would be appreciated. Thanks! [[alternative HTML version deleted]]

Dear all, For my PhD study I'm looking for relevant courses/workshops (short term) in ecological data anlysis with R in Europe. After 2 days searching I'm convinced that google is probably not the right medium to find this information. If anyone can help me I will be most grateful. Best regards - J. Capelle [[alternative HTML version deleted]]

Hi, I have developed a logistic regression model in the form of (factor_1~ numeric + factor_2) and would like to perform a cross-validation or some similar form of sensitivity analysis on this model. using cv.glm() from the boot package: # dataframe from which model was built in 'z' # model is called 'm_geo.lrm' # as suggested in the man page for a binomial model: cost <-

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

May I recommend my new book on mathematical modeling to you, which is based on R as a main software tool: Kai Velten: Mathematical Modeling and Simulation, Wiley-VCH, 2009, ISBN 978-3527407583. See also: http://www.wiley.com/WileyCDA/WileyTitle/productCd-3527407588.html http://books.google.com/books?id=Czp1N5UWpyEC List of reviews below. The book covers a broad range of mathematical models

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):

Stata users can rely on the very neat Episens package for sensitivity analysis. Briefly, it allows one to specify a diagnostic tool's sensitivity and specificity and take those into account when estimating a risk ratio, for instance. A full description of the package is available at <http://www.stata-journal.com/sjpdf.html?articlenum=st0138>

Hi! I am trying to implement the Morris One at a Time (OAT) sensitivity analysis technique in R using the package 'sensitivity'. The OAT and similar other techniques in the package requires data frame X (which stores the design or parameter combinations) to be populated by some function in the package such as fast99 or morris. These functions take only the range and step size of the

Hello list, I'm trying to carry out a global sensitivity analysis using the "sensitivity" package. I have a model with 26 paramters, mostly normally distributed (with a few truncated to not go below 0). I've been trying both the fast99 and sobol2007 functions, but having problems with both: * with fast99, points seem to be generated at the minimum and maximum values a

Dear Clara, Thanks for the reply. I am forwarding your message to the list, ok. When I wrote was a way of get further information to help the helpers. happy holidays, milton ---------- Forwarded message ---------- From: Clara Brück <clara_brueck@web.de> Date: 2009/12/30 Subject: Re: [R] Negbin Error Warnings To: milton ruser <milton.ruser@gmail.com> Dear Milton, Thanks for

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