A new function changeModelOnIC has been included to allow model selection to be based on the information criteria, rather than on hypothesis tests.
#ASREML R AR1 AT FULL#
An alternative is to use changeTerms.asrtests wiith IClikelihood set to REML or full and the information criteria will be included in the The test.summary of the asrtest object. There are now two infoCriteria methods, one for asreml objects and the other for lists of asreml objects. The infoCriteria method has been modified to add the possibility of calculating not only the the information criteria based on the REML likelihood, but those based on the full likelihood instead. This version represents a major revision in that the facilities for employing information criteria (AIC and BIC) have been considerabley expanded. Replace tol.diff with material.diff and set the default to 0.5 in changeModelOnIC.Īdd a new vignette for the use of information criteria with the wheat experiment.įixed bugs in changeModelOnIC associated with addFixed and dropFixed and with dealing with unconverged models. Removed the material.diff argument and the both option from the which.IC argument of changeModelOnIC.Ĭhange likelihood in infoCriteria to IClikelihood to make it consistent with other functions. Various bug fixes associated with asreml-R version 3.įix bug in processing a formula that includes an at function. Increment version number for resubmission to CRAN.įix bug in changeTerms when both addFixed and dropFixed are NULL.Īdded the IClikelihood argument to chooseModel.asrtests, reparamSigDevn.asrtests, rmboundary.asrtests, testranfix.asrtests, testresidual.asrtests, and testswapran.asrtests.įix bug in printFormulae when the formula is long. More details will be updated in the future.News for Package asremlPlus asremlPlus Version 4.2-18 ( # 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. Met.biplot( met2.asr, siteN =nlevels( MET $ Loc), VarietyN =nlevels( MET $ Genotype), faN = 2) 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) AAfun ::met.corr( met2.asr, site = MET $ Loc, faN = 2, kn = 2) Met3.asr <-asreml( yield ~ Loc, random = ~ Genotype :fa( Loc, 3), Met2.asr <-asreml( yield ~ Loc, random = ~ Genotype :fa( Loc, 2), MET $ yield <- 0.01 * MET $ yield #summary(MET$yield) met1.asr <-asreml( yield ~ Loc, random = ~ diag( Loc) : Rep + Genotype :fa( Loc, 2), # 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