cpgen (version 0.1)

get_cor: Compute the prediction accuracy from Cross Validition

Description

Takes a matrix of predictions returned by get_pred, a list of masked phenotypes returned by cCV and the original phenotype vector and returns the correlation between predicted and observed values

Usage

get_cor(predictions,cv_pheno,y)

Arguments

predictions

Prediction matrix returned by get_pred

cv_pheno

List of masked phenotypes returned by cCV

y

Original unmasked phenotype vector that has been used in cCV

Value

Numeric scalar - Mean prediction accuracy measured as correlation between predicted and observed phenotypes

See Also

clmm, get_pred, cCV

Examples

### 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)