similar to: Error message for corAR1()

Dear All Hopefully someone out there can point out what I am missing! I have a (large, several hundred) dataset of gardens in which over two years the presence/absence of a particular bird species is noted each week. I have good reason to believe there is a difference between the two years in the weekly proportion of gardens and would like to assess this, before going on to look in more detail at

Dear users I am fitting a Generalized Additive Mixed Models (gamm) model to establish possible relationship between explanatory variables (water temperature, dissolved oxygen and chlorophyll) and zooplankton data collected in the inner and outer estuarine waters. I am using monthly time-series which are auto-correlated. In the case of the inner waters, I have applied satisfactoryly (by

Dear everyone, I'm encountering an unknown error message when using gamm function: > fitoutput <- gamm(cvd~as.factor(dow)+pm10+s(time,bs="cr",k=15,fx=TRUE)+s(tmean,bs="cr",k=7,fx=TRUE) + ,correlation=corAR1(form=~1|city),family=poisson,random=list(city=~pm10),data=mimp) Maximum number of PQL iterations: 20 iteration 1 iteration 2 iteration 3 iteration 4

Greetings This is a long email. I'm struggling with a data set comprising 2,278 hydroacoustic estimates of fish biomass density made along line transects in two lakes (lakes Michigan and Huron, three years in each lake). The data represent lakewide surveys in each year and each data point represents the estimate for a horizontal interval 1 km in length. I'm interested in comparing

Hope one of you could help with the following question/problem: We would like to explain the spatial distribution of juvenile fish. We have 2135 records, from 75 vessels (code_tripnr) and 7 to 39 observations for each vessel, hence the random effect for code_tripnr. The offset (‘offsetter’) accounts for the haul duration and sub sampling factor. There are no extreme outliers in lat/lon. The model

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

Dear R_help list members!   I’m studding forest carnivores and I have data on resting site selection and use!   I’m trying to model the probability that a forest carnivore might have in be located in a tree hollow (1) (the main resting site selected) rather than elsewhere (0) (other resting site types, dens, nests, etc.). The model should be related with several variables such as for instance:  

Hello, I am trying to fit a gamm (package mgcv) model with a smooth term, a linear term, and an interaction between the two. The reason I am using gamm rather than gam is that there are repeated measures in time (which is the smooth term x1), so I am including an AR1 autocorrelation term. The model I have so far ended up with is of the type gamm(y ~ s(x1) + s(x1, by=x2), correlation =

Dear R-help users, I am trying to analyze some visual transect data of organisms to generate a habitat distribution model. Once organisms are sighted, they are followed as point data is collected at a given time interval. Because of the autocorrelation among these "follows," I wish to utilize a GAM-GEE approach similar to that of Pirotta et al. 2011, using packages 'yags' and

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

Dear R-helpers, I'd like to understand how to test the statistical significance of a random effect in gamm. I am using gamm because I want to test a model with an AR(1) error structure, and it is my understanding neither gam nor gamm4 will do the latter. The data set includes nine short interrupted time series (single case designs in education, sometimes called N-of-1 trials in medicine)

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+

Dear all, I have tried to calculate a GLMM fit with lmer (lme4) and glmmPQL (MASS), I also used glm for comparison. I am getting very different results from different functions, and I suspect that the problem is with our dataset rather than the functions, but I would appreciate help in deciding whether my suspicions are right. If indeed we are attempting the wrong type of analysis, some

Hi all R users ! I'm using gamm function from Simon Wood's mgcv package, to fit a spatial regression Generalized Additive Mixed Model, as covariates I have the geographical longitude and latitude locations of indexed data. I include a random effect for each district (dist) so the code is fit <- gamm(y~s(lon,lat,bs="tp", m=2)+offset(log(exp.)), random=list(dist=~1),

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

Hi! I would like to perform an F-Test over more than one variable within a generalized mixed model with Gamma-distribution and log-link function. For this purpose, I use the package mgcv. Similar tests may be done using the function "anova", as for example in the case of a normal distributed response. However, if I do so, the error message "error in eval(expr, envir, enclos) :

Dear R users, I have repeated measurements on individuals. I want to estimate the time-varying effect of a factor variable X (taking three levels), e.g. a model in the spirit of Hastie and Tibshirani (1993). I am considering using the package "mgvc" which implements generalized additive models, especially the function gamm, which estimates generalized additive mixed models, and thus,

Hello, I have a response variable that is a time series of 0's and 1's. And a couple of continous explanatory variables. I would like to fit a gamm with auto-correlation and binomial distribution using gamm in mgcv. Something simple like: tmp<-gamm(y ~ s(x), correlation=corAR1(), binomial) However, I read in various messages on this newsgroup that glmmPQL (used

Hello, I have two gamm question (I am using gamm in mgcv). 1. In have, say 5 time series. Monthly data, 20 year. The 5 time series are from 5 stations. The data are in vectors, so I have fitted something along the lines of: tmp<-gamm(Y ~ s(Year,by=station1)+s(Year,by=station2)+ s(Year,by=station3)+s(Year,by=station4)+

HELLO ALL, I AM GETTING AN ERROR MESSAGE WHEN TRYING TO RUN A GAMM MODEL LIKE THE ONE BELOW. I AM USING R VERSION 2.14.1 (2011-12-22) AND MGCV 1.7-12. M1 <-gamm(DepVar ~ Treatment + s(Year, by =Treatment), random=list(Block=~1), na.action=na.omit, data = mydata, correlation = corARMA(form =~ Year|Treatment, p = 1, q = 0)) THIS IS THE ERROR MESSAGE Error in `*tmp*`[[k]] : attempt to