In this instance, we have requested the adjusted means for all levels of variety, which are shown in the red rectangle together with their standard errors for the response variable means. “Removing Spatial Variation from Wheat Yield Trials: A Comparison of Methods.” Crop Science, 86, pp. Stroup WW, Baenziger PS and Mulitze DK (1994). Let’s see an example using the nin89 dataset. For predict.asreml(), your model term of interest will be referenced in the classify set. The output from ASReml-R forms predicted values for a factor and considers for the remaining variables, either user specified values of the remaining variables or average of these values. These predictions are sometimes called least-square means (LSMeans), but this term applies only to predictions from models without random effects. Predictions are formed as an extra process after the final iteration and they are primarily used for generating tables of adjusted means for all levels of a given model factor. The “ predict.asreml ()” command in ASReml-R forms a linear function of the vector of fixed and random effects to obtain a predicted value for a factor of interest. A “predict.asreml () ” function in ASReml-R
