An R function for linear mixed model analysis.

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Description

An R function for linear mixed model analysis with REML and/or MINQUE approaches

Usage

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lmm(formula,data = list(), method = NULL, ALPHA = NULL)

Arguments

formula

A linear mixed model formula.

data

Data frame. It can be default.

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.

Details

No data frame is needed when more than one response variables are analyzed

Value

Return list of simulated results for variance components

Author(s)

Jixiang Wu <qgtools@gmail.com>

References

Miller, R. G. 1974. The jackknife - a review. Biometrika, 61:1- 15.

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

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  library(minque)
  data(ncii)
  res=lmm(Yld~1|Female*Male+Rep,data=ncii,method=c("reml","minque"))
  res[[1]]$Var
  res[[1]]$FixedEffect
  res[[1]]$RandomEffect
  #End