adaa.simu: An R function for AD model simulation.

In qgtools: Generalized Quantitative Genetics Data Analyses

Description

An R function for linear mixed model simulation with generated data set and a given model.

Usage

adaa.simu(Y, Ped, method = NULL, ALPHA = NULL)

Arguments

Y	A matrix of simulated data set
Ped	A pedigree matrix including Environment, Female, Male, Generation, with or without block is required. So the matrix should include either 4 columns or 5 columns.
method	The default linear mixed model approach is MINQUE. Users can choose both or one of two linear mixed model approaches, REML and MINQUE.
ALPHA	A preset nominal probability level.The default is 0.05.

Value

Return list of simulated results for variance components.

Author(s)

Jixiang Wu <[e-mail]>

References

Rao, C.R. 1971. Estimation of variance and covariance components-MINQUE theory. J Multiva Ana 1:19

Rao, C. R. and Kleffe, J. 1980. Estimation of variance components. In Handbook of Statistics. Vol. l: 1-40. Krishnaiah, P. R. ed. New York. North-Holland.

Searle, S. R., Casella, G. and McCulloch, C. E. 1992. Variance Components. John Wiley & Sons, Inc. New York.

Wu J (2012) GenMod: An R package for various agricultural data analyses. ASA, CSSA, and SSSA 2012 International Annual Meetings, Cincinnati, OH, p 127

Wu J., Bondalapati K., Glover K., Berzonsky W., Jenkins J.N., McCarty J.C. 2013. Genetic analysis without replications: model evaluation and application in spring wheat. Euphytica. 190:447-458

Zhu J. 1989. Estimation of Genetic Variance Components in the General Mixed Model. Ph.D. Dissertation, NC State University, Raleigh, U.S.A

Examples

  library(qgtools)
  data(cotf2)
  dat=cotf2[which(cotf2$Env==1),]
  Ped=dat[,c(1:5)]
  Y=dat[,-c(1:5)]
  Y=data.frame(Y)
  #Ped=cotf2[,c(1:5)]
  #Y=cotf2[,-c(1:5)]
  YS=adaa.simudata(Y,Ped,v=rep(20,5),b=c(100),SimuNum=10)
  res=adaa.simu(YS,Ped,ALPHA=0.05)
  res
  ##End

qgtools documentation built on Dec. 19, 2019, 1:09 a.m.