similar to: fitting gamm with interaction term

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

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

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 have a dataset like this, > testdata Grouped Data: expr ~ visit | subject expr visit subject 1 6.502782 V1 A 2 6.354506 V1 B 3 6.349184 V1 C 4 6.386301 V2 A 5 6.376405 V2 B 6 6.758640 V2 C 7 6.414142 V3 A 8 6.354521 V3 B 9 6.396636 V3 C I tried the command >

Dear R-users, I am a dentist (so forgive me if my question looks stupid) and came across a problem when I did simulations to compare a few single level and two level regressions. The simulations were interrupted and an error message came out like 'Error in MEestimate(lmeSt, grps) : Singularity in backsolve at level 0, block 1'. My collegue suggested that this might be due to my codes

I'd like to compare tests based on the mixed model representation of additive models, testing among others y=f(x1)+f(x2) vs y=f(x1)+f(x2)+f(x1,x2) (testing for additivity) In mixed model representation, where X represents the unpenalized part of the spline functions and Z the "wiggly" parts, this would be: y=X%*%beta+ Z_1%*%b_1+ Z_2%*%b_2 vs y=X%*%beta+ Z_1%*%b_1+ Z_2%*%b_2 + Z_12

Hello, Excuse me for posting two questions in one day, but I figured it would be better to ask my questions in separate emails. I will again give the caveat that I'm not a statistician by training, but have a fairly decent understanding of probability and likelihood. As before, I'm trying to fit a nonlinear model to a dataset which has two main factors using nlme. Within the dataset

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

lme() and gls() models from the nlme package are all crashing with R.3.4.0. Identical code ran correctly, without error in R 3.3.3 and earlier versions. The behavior is easily demonstrated using one of the examples form the lme() help file, along with two simple variants. I have commented the errors generated by these calls, as well as the lines of code generating them, in the code example below.

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

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 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

Dear useRs, I am using the gamm function in the mgcv package to model a smooth relationship between a covariate and my dependent variable, while allowing for quantification of the subjectwise variability in the smooths. What I would like to do is to make subjectwise predictions for plotting purposes which account for the random smooth components of the fit. An example. (sessionInfo() is at

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 there, My question is about the 'theta' parameter in specification of a NB GAMM. I have fit a GAM with an optimum structure of: SB.gam4<-gam(count~offset(vol_offset)+ s(Depth_m, by=StnF, bs="cs")+StageF*RegionF, family=negbin(1, link=log), data=Zoop_2011[Zoop_2011$SpeciesF=='SB',]) However, this GAM shows heterogeneity in the

Hello:I run a gamm with following call :mode<-gamm(A~B,random=list(ID=~1),family=gaussian,na.action=na.omit,data=rs)an error happened:ERROR names(object$sp) <- names(G$sp) : attempt to set an attribute on NULLwith mgcv version 1.7-3What so? How can I correct the Error? Thanks very much for any help. [[alternative HTML version deleted]]

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

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, I am a bit confused about gamm in mgcv. Consulting Wood (2006) or Ruppert et al. (2003) hasn't taken away my confusion. In this code from the gamm help file: b2<-gamm(y~s(x0)+s(x1)+s(x2)+s(x3),family=poisson,random=list(fac=~1)) Am I correct in assuming that we have a random intercept here....but that the amount of smoothing is also changing per level of the