EVaddGam | R Documentation |
Computes the relative risk for additive gamma model at time 0
EVaddGam(theta, x1, x2, thetades, ags)
theta |
theta |
x1 |
x1 |
x2 |
x2 |
thetades |
thetades |
ags |
ags |
Thomas Scheike
Eriksson and Scheike (2015), Additive Gamma frailty models for competing risks data, Biometrics (2015)
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$theta.des) # dout$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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