search for: gamm4

Hello, I'm running mixed models in GAMM4 with 2 (non-nested) random intercepts and I want to include a spline term for one of my exposure variables. However, when I include a spline term, I always get reported degrees of freedom of less than 1, even when I know that my spline is using more than 1 degree of freedom. For example, here is...

My problem is relatively straight forward, but I cannot seem to find a way to make it work. I have a RCBD with repeated measurements over time. I have created a fit using the gamm4 package. My model is: fit4a <- gamm4(Rate ~ s(Time,by=trt,bs="cr")+trt,data=qual.11.dat, random=~(1|block),correlation=corARH1()) What I would like to create is plots with the X-axis being time, the Y-axis being the fitted Rates for each individual treatment with the...

..., by fitting splines. GLMMs/GAMMs are used to possibly improve fits from GLMs/GAMs by accounting for serial dependence. My idea is to use AIC to compare the different models. I’ve noticed that when setting up two seemingly identical models using the two functions gam (of the package mgcv) and gamm4 (of the package with same name), the AIC turns out to be different: > gam.0<-gam(dv ~ s(hours24,fx=F,k=-1,bs=“cc“),method="ML",data=sdata, family=binomial) > gamm.0<-gamm4(dv ~ s(hours24,fx=F,k=-1,bs=“cc“),method="ML",data=sdata, family=binomial) Fit indic...

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 us...

Dear all, I am writing as I would really need your help on the problem with gamm4. I have tried to find a solution online but I wasn't very successful. I am running a gamm4 model with an interaction between two variable using the tensor term, t2. I have a group variable (super end group) with six factors; I would like to run the model to see the how the interaction term var...

...1 Here is the summary of my block variable (indicating the number of groups for which autocorrelation exists within each block > summary(dat2$block) Min. 1st Qu. Median Mean 3rd Qu. Max. 1.00 39.00 76.00 73.52 111.00 148.00 However, I would like to use the package 'gamm4', as I am more familiar with Professor Simon Wood's packages and functions, and it appears gamm4 might be the most appropriate. It is important to note that the models have a binary response (organism presence of absence along a transect), and thus why I think gamm4 is more appropriate than...

Dear all,? I hope that you can help me on this. I have been struggling to figure this out but I haven't found any solution. I am running a generalised mixed effect model, gamm4, for an ecology project. Below is the code for the model: model<-gamm4(LIFE.OE_spring~s(Q95, by=super.end.group)+Year+Hms_Rsctned+Hms_Poaching+X.broadleaved_woodland? ? ? ? ? ? ?+X.urban.suburban+X.CapWks, data=spring, random=~(1|WATERBODY_ID/SITE_ID)) plot(model$gam, page=1, font.lab=2, xlab=&q...

Hi Maria If you have problems just start with a small model with predictions and then plot with xyplot the same applies to xyplot Try library(gamm4) spring <- dget(file = "G:/1/example.txt") str(spring) 'data.frame': 11744 obs. of 11 variables: $ WATERBODY_ID : Factor w/ 1994 levels "GB102021072830",..: 1 1 2 2 2 3 3 3 4 4 ... $ SITE_ID : int 157166 157166 1636 1636 1636 1635 1635 1...

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 = ~ (1|vpnr),data=sdata,...

Hi folks, I originally tried R-SIG-Mixed-Models for this one (https://stat.ethz.ch/pipermail/r-sig-mixed-models/2010q3/004170.html), but I think that the final steps to a solution aren't mixed-model specific, so I thought I'd ask my final questions here. I used gamm4 to fit a generalized additive mixed model to data from a AxBxC design, where A is a random effect (human participants in an experiment), B is a 2-level factor predictor variable, and C is a continuous variable that is likely non-linear. I tell gamm4 to fit a smooth across C to each level of B indep...

...be enough to conclude? I saw a preceding post on similar issues http://r.789695.n4.nabble.com/Comparing-and-Interpreting-GAMMs-td2234209.html but that example used simple s() smooths instead of tensor product smooths. In this case, the person was comparing between model A and model A1 like: A<-gamm4(Z~factor+s(x)+s(x,by=factor) ,data=DATA, random=~(1|Group)) A1<- gamm4(Z~factor+s(x) ,data=DATA, random=~(1|Group)) I thus also tried to compare my model gm1, with another gm2 model of that sort: gm2<-gamm(Z~te(x,y)+te(x,y,by=Factor)+Factor,data=DATA,random=list(Group=~1)) but this type of m...

...e, (b) is it linear or nonlinear in each case, and (c) does trend vary significantly over cases (PID), the latter presumably to be measured with a random effect. I can do the first two (not shown here), but am not sure about the third. I am using generalized additive models, either mgcv gam or gamm4. For example, using mgcv gam my syntax is M1 <- gam(DVY ~ s(SessIDX, bs = "re") + factor(TX), data = PCP, family = quasipoisson(link="log"), method="REML") summary(M1,all.p=TRUE) gam.vcomp(M1) Using gamm4, my syntax is PCP$fPID <- fact...

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) from one study. They report a proportion as outcome (y), computed from a behavior either observed or not out of 10 trials per time point. H...

...,x2)+ te(x1,x2,by=Factor1)+ te(x1,x2,by=Factor2)+s(Individual,bs="re"),dat=Data,family=binomial(link=logit),method="REML") 1)First question: is it OK to use gam() to model a binary variable with random effects coded as a "bs="re" term"?? I have read that the gamm4() function gives better performance than gamm() to deal with binary variables when random effects are coded as: random=~(1|Individual) but does that mean that binary variables should not be used as response variable in gam() with random effects coded as bs="re"??? 2)Second question: For...

...(e.g. SHRUB_CV) with the response variable it leads me to the gamm option… I start the model building procedure by finding firstly the best fixed structure and after the best random effect structure and finally the best model.   I my case my first attempt final model was obtained by:   Model <- gamm4(TREE_01 ~ s(Day) + T_MAX + SHRUB_CV + D_AV5RS_KM + D_RIP_KM + FOREST_PP, random = ~ (1 | fAnimal), method = "REML", control = lmc, family = binomial, data = RSTEMP_01_X2) summary(Model$gam)   Family: binomial Link function: logit Formula: TREE_01 ~ s(Day) + T_MAX + SHRUB_CV + D_AV5RS_K...

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...e looks like this: mod <- lmer(aim ~ bs(time) + var1 + var2 + factor(var3) +... +factor(var12) + (1|ID) ,family=binomial(link = "logit"), data=data) It works, but what I also want to do is plotting the time-spline, and I just can't find out how to do this. I also tried a gamm4 and a amer function, but I think they can't handle all the NA-values in the dataset as I always get error messages when I put all the variables into the model ("Matrices must have same number of columns in rbind2(..1, r)"). I'd be really thankful if someone could help me on this...

Hi all, Sorry to bother you. I'm estimating a discrete choice model in R using the maxBFGS command. Since I wrote the log-likelihood myself, in order to double check, I run the same model in Limdep. It turns out that the coefficient estimates are quite close; however, the standard errors are very different. I also computed the hessian and outer product of the gradients in R using the

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)

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