similar to: Zero inflated GAMM

Hi, I was reading the book on "Mixed Effects Models and Extensions in Ecology with R" by Zuur et al. In Section 6.2, an example is discussed where a gamm-model is fitted, with a smoother for time, which differs for each value of ID (4 different bird species). In earlier versions of R, the following code was used BM2<-gamm(Birds~Rain+ID+

 Hello everyone, Apologies in advance, as this is partially a stats question and partially an R question.  I have been using a GAM to model the activity level of bats going into and coming out from a forested edge.  I had eight microphones set up in a line transect at each of eight sites, and I am hoping to construct a model for each of 7 species.  My count data has a reverse J-shaped skew and

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

Greetings! I am looking for advice regarding the best way to compare GAMMs. I know other model outputs return enough information for R's AIC, ANOVA, etc. commands to function, but this is not the case with GAMM unless one specifies the gam or lme portion. I know these parts of the gamm contain items that will facilitate comparisons between gamms. Is it correct to simply use these values

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]]

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.

Hello, I am having some difficulty converting my gam code to a correct gamm code, and I'm really hoping someone will be able to help me. I was previously using this script for my overdispersed gam data: M30 <-gam(efuscus~s(mic, k=7) +temp +s(date)+s(For3k, k=7) + pressure+ humidity, family=negbin(c(1,10)), data=efuscus) My gam.check gave me the attached result. In order to

Hi list, I'm working with gamm models of this sort, using Simon Wood's mgcv library: gm<- gamm(Z~te(x,y),data=DATA,random=list(Group=~1)) gm1<-gamm(Z~te(x,y,by=Factor)+Factor,data=DATA,random=list(Group=~1)) with a dataset of about 70000 rows and 110 levels for Group in order to test whether tensor product smooths vary across factor levels. I was wondering if comparing those two

Hi, I am using GAMMs to show a relationship of temperature differential over time with a model that looks like this:- gamm(Diff~s(DaysPT)+AirToC,method="REML") where DaysPT is time in days since injury and Diff is repeat measures of temperature differentials with regards to injury sites compared to non-injured sites in individuals over the course of 0-24 days. I use the following

My GAMM model is to find drivers of species richness in forests is gamm1<- gamm(Total Species Richness ~ fROT + s(PH) + s(LOI) + ASP + s(SQRT_ELEV) + CANCOV + s(SQRT_TOTCWD) + s(WELLF) + s(WELLN) + s(OLDWDLD) + s(DISTWOOD) + s(Annprec), random=list (fSITE =~1), family = poisson, data = BIOFOR3) My issues are that the validation graphs are using methods I'm unfamiliar with e.g. square

Greetings all The help file for GAMM in mgcv indicates that the log likelihood for a GAMM reported using summary(my.gamm$lme) (as an example) is not correct. However, in a past R-help post (included below), there is some indication that the likelihood ratio test in anova.lme(mygamm$lme, mygamm1$lme) is valid. How can I tell if anova.lme results are meaningful (are AIC, BIC, and logLik

Dear R users I have a question related to the interpretation of results based on GAMMs using Simon Woods package gamm4. I have repeated measurements (hours24) of subjects (vpnr) and one factor with three levels (pred). The outcome (dv) is binary. In the first model I'd like to test for differences among factor levels (main effects only): gamm.11<-gamm4(dv ~ pred +s(hours24), random = ~

Hi, I've been using gamm4 to build GAMMs for exploring environmental influences on genetic ancestry. Things have gone well and I have 2 very straightforward questions: 1. I've used method=REML. Am I correct that this is an alternative method for estimating the smooth functions in GAMMs rather than GCV that is often used for GAMs? I've read up on REML and it makes sense, but I'm

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 am using the "mgcv" package by Simon Wood to estimate an additive mixed model in which I assume normal distribution for the residuals. I would like to test this model vs a standard parametric mixed model, such as the ones which are possible to estimate with "lme". Since the smoothing splines can be written as random effects, is it correct to use an (approximate)

Dear R users, I am trying to use "gamm" from package "mgcv" to model results from a mesocosm experiment. My model is of type M1 <- gamm(Resp ~ s(Day, k=8) + s(Day, by=C, k=8) + Flow + offset(LogVol), data=MyResp, correlation = corAR1(form= ~ Day|Mesocosm), family=poisson(link=log)) where the response variable is counts, offset by the

Hello I'm analyzing a dichotomous dependent variable (dv) with more than 100 measurements (within-subjects variable: hours24) per subject and more than 100 subjects. The high number of measurements allows me to model more complex temporal trends. I would like to compare different models using GLM, GLMM, GAM and GAMM, basically do demonstrate the added value of GAMs/GAMMs relative to

Dear All, I have a query relating to the use of the ?predict? and ?gamm? functions. I am dealing with large (approx. 5000) sets of presence/absence data, which I am trying to model as a function of different of environmental covariates. Ideally my models should include individual and colony as random factors. I have been trying to fit binomial models using the gamm function to achieve this. For

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):