ad4.mq.jack: Additive-dominance (AD) model, multi-parent mating designs,...
In qgtools: Generalized Quantitative Genetics Data Analyses

Description Usage Arguments Details Value Author(s) References Examples

An AD model from multi-parent mating designs can be analyzed by MINQUE and jackknife methods, requiring no specific genetic mating designs or balance data.

1	ad4.mq.jack(Y, Ped, JacNum = NULL, JacRep = NULL)

`Y`	A trait matrix including one or more than one traits.
`Ped`	A pedigree matrix including Environment, Female1, Male1, Female2, Male2, Generation, with or without block is required. So the matrix should include either 6 columns or 7 columns.
`JacNum`	Number of groups to be jackknifed. The default is 10
`JacRep`	Number of jackknife process to be repeated. The default is 1.

A pedigree matrix used for analysis is required in the order of Environment (column 1), Female1(column 2), Male1(column 3), Female2(column 4), Male2(column 5),Generation (column 6). Column 7 for block can be default. Even though there is only one environment, this column is needed.

Return a list of results: estimated variance components, estimated proportional variance components, estimated fixed effects, and predicted random effects

Jixiang Wu <qgtools@gmail.com>

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

Wu, J., McCarty Jr., J.C., Jenkins, J.N. 2010. Cotton chromosome substitution lines crossed with cultivars: Genetic model evaluation and seed trait analyses. Theoretical and Applied Genetics 120:1473-1483.

Wu, J., J. N. Jenkins, J. C. McCarty, K. Glover, and W. Berzonsky. 2010. Presentation titled by "Unbalanced Genetic Data Analysis: model evaluation and application" was offered at ASA, CSSA, & SSSA 2010 International Annual Meetings, Long Beach, CA.

Wu, J., J. N. Jenkins, and J.C., McCarty. 2011. A generalized approach and computer tool for quantitative genetics study. Proceedings Applied Statistics in Agriculture, April 25-27, 2010, Manhattan, KS. p.85-106.

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(wheat)
 n=nrow(wheat)
 id=sample(n,200)
 dat=wheat[id,]
 Ped=dat[,c(1:6)]
 Y=as.matrix(dat[,8])
 colnames(Y)=colnames(dat)[8]
 
 res=ad4.mq.jack(Y,Ped,JacNum=5)
 res$Var
 res$PVar
 res$FixedEffect
 res$RandomEffect

 ##End