Description Usage Arguments Details Value Author(s) References See Also Examples
Function for the application of the cross validation procedure on prediction models with fixed and random effects. Covariance matrices must be committed to the function and variance components can be committed or reestimated with ASReml or the BLR function.
1 2 3 4 
gpData 
Object of class 
trait 

cov.matrix 

k 

Rep 

Seed 

sampling 
Different sampling strategies can be 
TS 
A (optional) list of vectors with IDs for the test set in each fold within a list of replications, same layout as output for 
ES 
A (optional) list of IDs for the estimation set in each fold within each replication. 
varComp 
A 
popStruc 
Vector of length 
VC.est 
Should variance components be reestimated with " 
verbose 
Logical. Whether output shows replications and folds. 
... 
further arguments to be used by the genomic prediction models, i.e. prior values and MCMC options for the 
In cross validation the data set is splitted into an estimation (ES) and a test set (TS). The effects are estimated with the ES and used to predict observations in the TS. For sampling into ES and TS, kfold cross validation is applied, where the data set is splitted into k subsets and k1 comprising the ES and 1 is the TS, repeated for each subset.
To account for the family structure (Albrecht et al. 2011), sampling
can be defined as:
Does not account for family structure, random sampling within the complete data set
Accounts for within population structure information, e.g. each family is splitted into k subsets
Accounts for across population structure information, e.g. ES and TS contains a set of complete families
The following mixed model equation is used for VC.est="commit"
:
y=Xb+Zu+e
with
u=N(0,Gsigma2u)
gives the mixed model equations
(X'X,X'Z,Z'X,ZZ+GIsigma2e/sigma2u)(b,u)=(X'y,Z'y)
An object of class list
with following items:
bu 
Estimated fixed and random effects of each fold within each replication. 
n.DS 
Size of the data set (ES+TS) in each fold. 
y.TS 
Predicted values of all test sets within each replication. 
n.TS 
Size of the test set in each fold. 
id.TS 
List of IDs of each test sets within a list of each replication. 
PredAbi 
Predictive ability of each fold within each replication calculated as correlation coefficient r(y_{TS},\hat y_{TS}). 
rankCor 
Spearman's rank correlation of each fold within each replication calculated between y_{TS} and \hat y_{TS}. 
mse 
Mean squared error of each fold within each replication calculated between y_{TS} and \hat y_{TS}. 
bias 
Regression coefficients of a regression of the observed values on the predicted values in the TS. A regression coefficient < 1 implies inflation of predicted values, and a coefficient of > 1 deflation of predicted values. 
m10 
Mean of observed values for the 10% best predicted of each replication. The k test sets are pooled within each replication. 
k 
Number of folds 
Rep 
Replications 
sampling 
Sampling method 
Seed 
Seed for 
rep.seed 
Calculated seeds for each replication 
nr.ranEff 
Number of random effects 
VC.est.method 
Method for the variance components ( 
Theresa Albrecht
Albrecht T, Wimmer V, Auinger HJ, Erbe M, Knaak C, Ouzunova M, Simianer H, Schoen CC (2011) Genomebased prediction of testcross values in maize. Theor Appl Genet 123:339350
Mosier CI (1951) I. Problems and design of crossvalidation 1. Educ Psychol Measurement 11:511
Crossa J, de los Campos G, Perez P, Gianola D, Burgueno J, et al. (2010) Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers, Genetics 186:713724
Gustavo de los Campos and Paulino Perez Rodriguez, (2010). BLR: Bayesian Linear Regression. R package version 1.2. http://CRAN.Rproject.org/package=BLR
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  # loading the maize data set
## Not run:
library(synbreedData)
data(maize)
maize2 < codeGeno(maize)
U < kin(maize2,ret="realized")
# cross validation
cv.maize < crossVal(maize2,cov.matrix=list(U),k=5,Rep=1,
Seed=123,sampling="random",varComp=c(26.5282,48.5785),VC.est="commit")
cv.maize2 < crossVal(maize2,k=5,Rep=1,
Seed=123,sampling="random",varComp=c(0.0704447,48.5785),VC.est="commit")
# comparing results, both are equal!
cv.maize$PredAbi
cv.maize2$PredAbi
summary(cv.maize)
summary(cv.maize2)
## End(Not run)

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