Hi, I am trying to fit a model with a random effect of DeploymentID with a nested AR1 autoregressive correlation structure. For the fixed component I am fitting a smooth of tide. I have two sets of models I am fitting with different data sets. For the smooth of tide, I want a separate smooth to be fitted per SiteID. In one set of models this is fine (each SiteID contains multiple DeploymentIDs). In the other SiteID and DeploymentID are identical. I am wondering how to code this. I am not interested in the intercept of SiteID hence why it has previously been a random effect. I am interested in how smooths vary between SiteIDs and hence why this is a fixed effect.
Example data structure first data set: SiteID DeploymentID 1 1 1 1 1 1 1 1 1 1 1 2 1 2 1 2 1 3 2 4 2 4 2 4 2 4 2 4 2 5 2 5 2 5 3 6 3 7 3 8 etc etc Example data structure seconddata set: SiteID DeploymentID 1 1 2 2 3 3 4 4 My problem is that I understand to fit a interaction term, one must use gamm(Y~s(tide,k=5,bs="cc",by=SiteID)+SiteID,knots=list(tide=c(0,1)),correlation=corAR1(form=~1|DeploymentID)..... - if I include +SiteID then I should NOT include DeploymentID as a random effect also (for the second model where SiteId and DeploymentID are identical - but this is ok for the first model)? -the problem is when I want to compare nested models I run into issues if the smooth term is dropped as I do not have a random or smooth term in the model. Can I code as gamm(Y~s(tide,k=5,bs="cc",by=SiteID),knots=list(tide=c(0,1)),random=list(DeploymentID=~1),correlation=corAR1(form=~1|DeploymentID)..... Any help on this is appreciated.... Cheers -- View this message in context: http://r.789695.n4.nabble.com/Fitting-interaction-term-in-GAMM-with-random-effect-tp4719322.html Sent from the datatable-help mailing list archive at Nabble.com. _______________________________________________ datatable-help mailing list [email protected] https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/datatable-help
