MTGS (version 0.1.0)

MTGS.mlasso: Genomic Selection using Multivariate LASSO (MLASSO)

Description

Calculates the Genomic Estimated Breeding Value based on MLASSO method.

Usage

MTGS.mlasso(X, Y, r)

Arguments

X

X is a design matrix of marker genotype of size n<U+00D7>p, where n are no of Individuals under study (i.e. genotype, lines) and p are no of markers.

Y

Y is matrix of individuals (n) with their phenotypic traits (q) of size n<U+00D7>q.

r

r is fraction of testing data (ranges from (0-1)) used during model fitting(suppose if one want to use 75% of data for model training and remaining 25% for model testing so one has to define r=0.25).

Value

$fit Lists various coeffecient assocaited to LASSO model fitting.

$Pred GEBV's for genotype/individuals under study.

References

Tibshirani, R. 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Series B (Methodological). 267<U+2013>288.

Searle, S.R., G. Casella and C.E. McCulloch. 1992. Variance Components. John Wiley, Hoboken.

Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent, https://web.stanford.edu/~hastie/Papers/glmnet.pdf Journal of Statistical Software, Vol. 33(1), 1-22 Feb 2010 http://www.jstatsoft.org/v33/i01/.

Jerome Friedman, Trevor Hastie, Robert Tibshirani (2010). Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, 33(1), 1-22. URL http://www.jstatsoft.org/v33/i01/.

Examples

# NOT RUN {
library(MTGS)
data(brassica_data)
X<-brassica_data[,1:100]
Y<-brassica_data[,101:103]
r<-0.25
MTGS.mlasso(X,Y,r)

# }