lam0 <- c(0.5,0.3)
pars <- c(1,1,1,1,0,1)
## genetic random effects, cause1, cause2 and overall
parg <- pars[c(1,3,5)]
## environmental random effects, cause1, cause2 and overall
parc <- pars[c(2,4,6)]
## simulate competing risks with two causes with hazards 0.5 and 0.3
## ace for each cause, and overall ace
out <- simCompete.twin.ace(10000,parg,parc,0,2,lam0=lam0,overall=1,all.sum=1)
## setting up design for running the model
mm <- familycluster.index(out$cluster)
head(mm$familypairindex,n=10)
pairs <- matrix(mm$familypairindex,ncol=2,byrow=TRUE)
tail(pairs,n=12)
#
kinship <- (out[pairs[,1],"zyg"]=="MZ")+ (out[pairs[,1],"zyg"]=="DZ")*0.5
dout <- make.pairwise.design.competing(pairs,kinship,
type="ace",compete=length(lam0),overall=1)
head(dout$ant.rvs)
## MZ
dim(dout$theta.des)
dout$theta.des[,,1]
dout$random.design[,,1]
## DZ
dout$theta.des[,,nrow(pairs)]
dout$random.design[,,nrow(pairs)]
#
thetades <- dout$theta.des[,,1]
x <- dout$random.design[,,1]
x
##EVaddGam(rep(1,6),x[1,],x[3,],thetades,matrix(1,18,6))
thetades <- dout$theta.des[,,nrow(out)/2]
x <- dout$random.design[,,nrow(out)/2]
##EVaddGam(rep(1,6),x[1,],x[4,],thetades,matrix(1,18,6))
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