# dLam.u - least distance from center # dSco.u - least score of Variety breeding value # if can not draw fig 3, try multiplying or being devided by 10 for aim trait data. For each simulated data set we then fitted a linear mixed effect model (using ASReml-R) containing individual as a random effect resulting in an estimate of. Asreml-R asreml-R is the R interface to the ASReml tting routines.
ASREML R SOFTWARE
Both versions of the ASReml statistical analysis software are powerful tools for fitting mixed models and are based on the same computational core. Met.biplot( met2.asr, siteN =nlevels( MET $ Loc), VarietyN =nlevels( MET $ Genotype), faN = 2) ASReml-R is more convenient for regular users of the R environment and it is easier to manipulate data. Met.biplot( met3.asr, siteN = 6, VarietyN = 36, faN = 3) # biplot asreml-met results AAfun ::met.biplot( met2.asr, siteN = 6, VarietyN = 36, faN = 2) Met.corr( met3.asr, site = MET $ Loc, faN = 3, kn = 2)
Met.corr( met1.asr, site = MET $ Loc, faN = 2, kn = 2) use asremls controlling stuff - the ntrol() function control many aspects of asreml call, you can provide its arguments directly to asreml call.
AAfun ::met.corr( met2.asr, site = MET $ Loc, faN = 2, kn = 2) I am using ASREML-R to fit unstructured (UN) and factor analytic (FA) model to explore complex structure of genotype by environment interaction in multienvironment yield data. Met3.asr <-asreml( yield ~ Loc, random = ~ Genotype :fa( Loc, 3), Met2.asr <-asreml( yield ~ Loc, random = ~ Genotype :fa( Loc, 2),
ASREML R HOW TO
Genomic Best Linear Unbiased Prediction, or GBLUP, is a genomic selection method that uses genetic relations. Tutorial 1 (ASReml-R) - Estimati ng the heritability of birth weight This tutorial will demonstrate how to run a univariate animal model using the software ASRemlR and example data files provided. MET $ yield <- 0.01 * MET $ yield #summary(MET$yield) met1.asr <-asreml( yield ~ Loc, random = ~ diag( Loc) : Rep + Genotype :fa( Loc, 2), To see downloadable files click SHOW MORE below. # plot MET data - example 2 MET3 <- MET # add variable order on MET2: Rep, Block # plot MET data - example 1 # variable order: genotype,yield,site,row,col MET2 <- MET # met.plot(): plots MET data # met.corr(): calculate var/cov/corr from asreml MET factor analytic results # met.biplot(): biplots MET factor analytic results from asreml
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