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15 Examples 263variety 25yieldbarley.asd !skip 1 !DOPATH 1!PATH 1 # AR1 x AR1y ~ mu varpredict var1 215 column AR1 0.1 # Second field is specified so ASReml can sort10 row AR1 0.1 # records properly into field order!PATH 2 # AR1 x AR1 + unitsy ~ mu var !r unitspredict var1 215 column AR1 0.110 row AR1 0.1!PATH 3 # incomplete blocksy ~ mu var !r Rep Rowblk Colblkpredict varAbbreviated ASReml output file is presented below. The iterative sequence hasconverged to column and row correlation parameters of (.68377,.45859) respectively.The plot size and orientation is not known and so it is not possible toascertain whether these values are spatially sensible. It is generally found thatthe closer the plot centroids, the higher the spatial correlation. This is not alwaysthe case and if the highest between plot correlation relates to the larger spatialdistance then this may suggest the presence of extraneous variation (see Gilmouret al., 1997), for example. Figure 15.5 presents a plot of the sample variogramof the residuals from this model. The plot appears in reasonable agreement withthe model.The next model includes a measurement error or nugget effect component. Thatis the variance model for the plot errors is now given byσ 2 Σ = σ 2 (Σ c ⊗ Σ r ) + ψI 150 (15.6)where ψ is the ratio of nugget variance to error variance (σ 2 ). The abbreviatedoutput for this model is given below. There is a significant improvement in theREML log-likelihood with the inclusion of the nugget effect (see Table 15.7).# AR1 x AR1#1 LogL=-739.681 S2= 36034. 125 df 1.000 0.1000 0.1000