get_cor | R Documentation |
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
get_cor(predictions,cv_pheno,y)
predictions |
Prediction matrix returned by |
cv_pheno |
List of masked phenotypes returned by |
y |
Original unmasked phenotype vector that has been used in |
Numeric scalar - Mean prediction accuracy measured as correlation between predicted and observed phenotypes
clmm, get_pred, cCV
### Running a 4-fold cross-validation with one repetition:
# generate random data
rand_data(100,500)
### 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=TRUE))) * 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)
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