### Running a 4-fold cross-validation with one repetition:
## Not run:
#
# # generate random data
# rand_data(500,5000)
#
# ### compute the list of masked phenotype-vectors for CV
# y_CV <- cCV(y,fold=4,reps=1)
#
#
# ### Cross Validation using GBLUP
# G.A <- cgrm.A(M,lambda=0.01)
#
#
# ### generate the list of design matrices for clmm
# Z_list = list(t(chol(G.A)))
#
# ### specify options
# h2 = 0.3
# scale = unlist(lapply(y_CV,function(x)var(x,na.rm=T))) * h2
# df = rep(5,length(y_CV))
# par_random = list(list(method="ridge",scale=scale,df=df))
#
# ### run
# fit <- clmm(y_CV, Z=Z_list, par_random=par_random, niter=5000, burnin=2500)
#
# ### inspect results
# str(fit)
#
# ### obtain predictions
# pred <- get_pred(fit)
#
# ### prediction accuracy
# get_cor(pred,y_CV,y)
# ## End(Not run)
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