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MCMCglmm Poisson with an offset term and splines

dani 
Hello Jarrod,

I have attached my code and my file.

Variable age has 3 levels, variables x2, x8, and x9 have two levels each, and the rest of the variables are continuous.

I re-ran the spl2 function and this time around I obtained one single fixed smoother (l_lfvcspo) and one single random smoother (l_lfvcspn). Last time around I got 8 variables with suffixes from 1-8 for l_lfvcspn and I did not know what to do with those.

Also, as I had 11 fixed effects (corresponding to 11 variables), I thought it was appropriate to choose k=11. I was not sure whether the intercept needed to be counted as well as the levels of the categorical fixed predictors (except for their reference categories). Because I kept on getting the "mu" error for k=11, I tried k=13 (which includes the intercept and the 2 levels for the 3-level age variable, which I did not consider before). I am not sure this is the way to go, I guess I need to read more to be able to model properly fixed effects in R, but for now I was wondering whether this consideration of fixed effects sounds ok in this particular example.

The MCMC model worked properly based on the prior using k=13.

Please see the code below:

#spl2 function

library(mgcv)

spl2<-function(formula, data, p=TRUE, dataX=data){

  aug<-nrow(data)-nrow(dataX)

  if(aug!=0){
    if(aug<0){
      stop("sorry nrow(dataX) must be less than or equal to nrow(data)")
    }else{
      augX<-matrix(0, aug, ncol(dataX))
      colnames(augX)<-colnames(dataX)
      dataX<-rbind(dataX, augX)
    }
  }
  smooth.spec.object<-interpret.gam(formula)$smooth.spec[[1]]
  sm<-smoothCon(smooth.spec.object, data=data, knots=NULL,absorb.cons=TRUE, dataX=dataX)[[1]]

  Sed<-eigen(sm$S[[1]])
  Su<-Sed$vectors
  Sd<-Sed$values
  nonzeros <- which(Sd > sqrt(.Machine$double.eps))

  if(p){
    Zn<-sm$X%*%Su[,nonzeros, drop=FALSE]%*%diag(1/sqrt(Sd[nonzeros]))
  }else{
    Zn<-sm$X[,-nonzeros, drop=FALSE]
  }
  return(Zn[1:(nrow(data)-aug),,drop=FALSE])
}

$spline terms

newdatab1$l_lfvcspo<-spl2(~s(f_lfv_c,k=10), data=newdatab1, p=F)
newdatab1$l_lfvcspn<-spl2(~s(f_lfv_c,k=10), data=newdatab1)
summary(newdatab1$l_lfvcspo)
summary(newdatab1$l_lfvcspn)

summary(newdatab1)
dim(newdatab1)
str(newdatab1)

#PRIOR


k<-13 # number of fixed effects

prior<-list(B=list(V=diag(k)*1e4, mu=rep(0,k)),
            R=list(V=1, nu=0),
            G=list(G1=list(V=1, nu=0),
                   G2=list(V=1, nu=0),
                   G3=list(V=1, nu=0)))

prior$B$mu[k]<-1 # assuming the offset term is last
prior$B$V[k,k]<-1e-4

# MCMC model

mcmc <- MCMCglmm(y ~ age+x2+x8+x9+x3+l_lfvcspo+x4+x5+x6+x7+offset,
                 random =~ STUDYID+class+idv(l_lfvcspn),
                 data   = newdatab1,
                 family = "poisson", prior=prior)
summary(mcmc)



This model has worked, so I would like to thank you so much for all your help so far. I guess I just want to make sure I understand how to model the fixed effects in MCMCglmm and I also would like to make sure that my model is correct.

I truly appreciate all your invaluable help!
Best regards,
Dani NM
<http://aka.ms/weboutlook>

Thread (15 messages)

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