#Data generated by
## Not run:
# set.seed(20)
# n <- 600 #observations per cohort
# d <- 2000 #SNPs
# k <- 100 #genes
#
# ##### First cohort of unrelated individuals:
# Z1 <- replicate(d,rbinom(n,2,rbeta((n),3,200)))
#
# ## assign SNP id's to the columns
# colnames(Z1) <- sample(d+50,d) + 1e6
#
# pheno1 <- data.frame("y" = rnorm(n), "sex"=rep(1:2,(n/2)), "bmi"=rnorm(n,25,2),
# "ybin" = rbinom(n,1,.5), "time"=rpois(n,5), "status"=rbinom(n,1,.9))
#
# genes <- paste0("gene",1:k)
# SNPInfo <-data.frame("Name"=as.character(1:(d+50) + 1e6),
# "gene"=sort(sample(genes,d+50,replace=T)), stringsAsFactors=FALSE)
# #SNPInfo <- data.frame("Name" =1:(d+50) + 1e6, "gene" = sort(sample(genes,d+50,replace=T)))
#
# #####Second cohort of family data:
# # 150 families of size 4
# require(kinship2)
# fullped<-data.frame(famid=rep(1:(n/4),each=4),id=10001:(10000+n),fa=rep(0,n),mo=rep(0,n))
# fullped$fa[(1:(n/4))*4-1]<-fullped$fa[(1:(n/4))*4]<-(1:(n/4))*4+9997
# fullped$mo[(1:(n/4))*4-1]<-fullped$mo[(1:(n/4))*4]<-(1:(n/4))*4+9998
# kins = makekinship(fullped$famid, fullped$id, fullped$fa, fullped$mo)
#
# ## generate a phenotype with 20% `heritability':
# pheno2<-data.frame("id"=10001:(10000+n),"y"=t(rnorm(n)%*%chol(0.2*2*as.matrix(kins) +
# 0.8*diag(n))),"sex"=rep(1:2,(n/2)),"bmi"=rnorm(n,25,2))
#
# Z2 <- replicate(d,rbinom(n,2,rbeta((n/4),3,200)[fullped$famid]))
# colnames(Z2) <- sample(d+50,d) + 1e6
# ## End(Not run)
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