ASReml-S reference manual - VSN International

3.5 A note on data order 23• the REML log-likelihood,• best linear unbiased predictors (BLUPs) of the random effects,• generalised least squares estimates of the fixed effects,• REML estimates of variance components,• (optionally) part of the inverse coefficient matrix,• the inverse of the average information matrix,• residuals and fitted values from the linear model.A complete description of the components of an asreml object are given in Section 7.2.summary()Variance componentsCoefficients3.4.1 Methods and related functionsSpecific instances of the standard extractor functions coef(), resid() and fitted() exist, asdo summary(), plot() and predict() (see Chapter 6) methods. An anova type method isimplemented by wald() (see Section 3.16),The summary.asreml() function returns a list with a range of components:> names(summary(nin89.asr))[1] "call" "distribution" "link" "loglik"[5] "nedf" "sigma" "deviance" "heterogeneity"[9] "varcomp" "coef.fixed" "coef.random" "coef.sparse"[13] "residuals"The variance components are returned in> summary(nin89.asr)\$varcompgamma component std.error z.ratio constraintRep 0.1993231 9.882913 8.792685 1.123993 PositiveR!variance 1.0000000 49.582378 5.458841 9.082950 Positiveand the coefficients from the fixed, random and sparse parts of the model are summarisedin the coef.fixed, coef.random and coef.sparse components. For example, the fixed effectsfor Variety are given by> summary(nin89.asr)\$coef.fixedsolution std error z ratioVariety_ARAPAHOE 0.0000 NA NAVariety_BRULE -3.3625 4.979087 -0.675324649Variety_BUCKSKIN -3.8750 4.979087 -0.778255171...Variety_TAM200 -8.2000 4.979087 -1.646888363Variety_VONA -5.8375 4.979087 -1.172403758(Intercept) 29.4375 3.855601 7.6349964523.5 A note on data orderThe observations must be presented in the order specified by the error model, that is, thevalue of the rcov argument. The assumption of separability is implicit in the use of thecolon operator (:). Furthermore, the sort order outer:inner of the observations is impliedby the order of appearance of the factors in the rcov formula. In the case, for example,wherercov = ∼ ar1(Column):ar1(Row)the data is assumed to be sorted as rows within columns.Note that if the sort order of observations is incorrect an error is generated.