adm.simu.jack: An R function for ADM model simulation
In qgtools: Generalized Quantitative Genetics Data Analyses

Description Usage Arguments Value Author(s) References Examples

An R function for ADM model simulation with generated data set.

1	adm.simu.jack(Y, Ped, method = NULL, JacNum = NULL, JacRep = NULL, ALPHA = NULL)

`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.
`JacNum`	Number of jackknife groups. The default is 10.
`JacRep`	Repeating times for jackknife process. The default is 1.
`ALPHA`	A preset nominal probability level.The default is 0.05.

Return list of simulated results for variance components.

Jixiang Wu <qgtools@gmail.com>

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

  library(qgtools)
  data(cotf2)
 
  dat=cotf2[which(cotf2$Env==1),]
  Ped=dat[,c(1:5)]
  Y=dat[,6]
  Y=data.frame(Y)
  YS=adm.simudata(Y,Ped,v=rep(20,6),b=c(100),SimuNum=10)
  res=adm.simu.jack(YS,Ped,JacNum=5)
  res
  ##End