similar to: gamm question

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 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

Hi everyone. I have a question about a work on R I have to do for my job. I have temperature data coming from 70 weather stations. One data file corresponds to one station for one year (so 70 files for one year). Each file looks like this (important: each file contains NAs): time data 01/01/2008 00:00 -0.25 01/01/2008 00:15 -0.18 01/01/2008 00:30 -0.25 01/01/2008

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 =

In the summary of the gam object produced by gamm, the "Approximate significance of smooth terms" appears to be a test of the improvement in fit over a linear model, rather than a test of the significance of the overall effect of x on y: test.gamm<-gamm(y~te(x, bs="cr"), random=list(grp=~1)) summary(test.gamm$gam) . . . Approximate significance of smooth terms:

Dear R-users, To obtain the percentage of deviance explained when fitting a gam model using the mgcv library is straightforward: summary(object.gam) $dev.expl or alternatively, using the deviance (deviance(object.gam)) of the null and the fitted models, and then using 1 minus the quotient of deviances. However, when a gamm (generalizad aditive mixed model) is fitted, the

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

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

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

Hi, I have confirmed temporal correlation problems in my data. Is there a possibility to use corCompSymm for a gamm()? I am an R-beginner. I have very short time series. There are three years and within each year, there are 10 weeks. he 10 weeks are the same every year and have not unique values, I seem not to be able to use AR-1 (I assume that I have too little data for autoregression

Hi all, I would like to fit a gamm model of the form: Y~X+X*f(z) Where f is the smooth function and With random effects on X and on the intercept. So, I try to write it like this: gam.lme<- gamm(Y~ s(z, by=X) +X, random=list(groups=pdDiag(~1+X)) ) but I get the error message : Error in MEestimate(lmeSt, grps) : Singularity in backsolve at level 0, block 1

Hi I am an engineer and am running the package mgcv and specifically the command gamm (generalized additive mixed modelling), with random effects. i have a few queries: 1. When I run the command with 1000/2000 observations, it runs ok. However, I would like to see the results as in vis.gam command in the same package, with the 3-d visuals. It appears no such option is available for gamm in the

I want to use gamm to generate smoothed trend line for three groups identified by dummy variable genea and geneb. My question is how to add in an interaction term between the time and another dummy variable such as gender? fitm<-gamm(change_gfr~ genea+geneb+s(timea_n,bs="ps")+s(timeb_n,bs="ps")+s(timec_n,bs="ps"),data=mm,random=list(time_n=~1|PID)) -- View this

Dear all, I?m analyzing this dataset containing biodiversity indices, measured over time (Week), and at various contaminant concentrations (Treatment). We have two replicates (Replicate) per treatment. I?m looking for the effects of time (Week) and contaminant concentration (Treatment) on diversity indices (e.g. richness). Initial analysis with GAM models showed temporal autocorrelation of

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 group, I am trying to use gamm() in mgcv. Here's the scenario. The data frame has approx. 110K observations with information on paediatric readmission binary outcome (Y/N) and total volume of their most responsible physician as the covariate. Since any physician can have multiple patients, the data contains clustering structure which I am trying to account for. My original formula is

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

My data set consist of number of calls (lcin) across Day. I am looking for activity differences between three features (4 sites per feature). I am also looking for peaks of activity across time (Day). I am using a gamm since I believe these are nonlinear trends with nested data. gammdata<-gamm(lcin~Temp+s(Day)+fType+wind+fFeature+Forest+Water+Built, list=fSite,data=data, family=gaussian)

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