nxk PCA matrix from the predictor variables.

PCAs

vector of names for the population without the test set.

candidates

name of the individuals in the test or VP set.

Test

number of individuals to select in the training population.

ntoselect

npop

nelite

probability of mutation for each solution

mutprob

niterations

lambda


This function is a wrapper from the STPGA package to obtain the best subset of individuals to predict a group of individuals minimizing the predicted error variance (PEV). Is used internally in the TP.prep function.<p></p><p></p>

Multivariate mixed model solver for multiple random effects allowing the specification of variance covariance structures. ML/REML estimates are obtained using the Average Information (AI), Expectation-Maximization (EM), Newton-Raphson (NR), or Efficient Mixed Model Association (EMMA) algorithms. Designed for genomic prediction and genome wide association studies (GWAS) to include additive, dominance and epistatic relationship structures or other covariance structures in R, but also functional as a regular mixed model program. Multivariate models (multiple responses) can be fitted currently with NR, AI and EMMA algorithms allowing multiple random effects as well. Covariance structures for the residual component is currently supported only for balanced univariate Newton Raphson.

Giovanny CovarrubiasPazaran

sommer

Solving Mixed Model Equations in R

PEV function


This function is a wrapper from the STPGA package to obtain the best subset of individuals to predict a group of individuals minimizing the predicted error variance (PEV). Is used internally in the TP.prep function.



PEV: Selecting the best training population for genomic selection

Description

Usage

Arguments

Value

References

See Also