This is program is used to do cross-validations. It reads a blupf90 parameter file, a solutions file and a data file. It needs OPTION include_effects followed by a series of effects. It computes

• y_star = y adjusted by the other (not included) effects
• y_hat = sum of estimates of the included effects
• residual = y - included effects (not a true residual)

For instance consider

y = herd + age + animal + e

with OPTION include_effects 3.

• y_star = y - herd_hat - age_hat
• y_hat = animal_hat

Which makes cor(y_hat,y_star) = cor(ebv, adjusted y), in this example, which is a measure of accuracy.

It outputs the correlation between y_hat and y_star, for instance, cor(ystar,yhat)=cor(u+e, uhat) and outputs these columns into a file, together with animal id (if there is animal in the model) or record number (if not).

In addition, if animal effect is in the model, it produces a file with ebvs from the solutions file.

This program splits the phenotype (y) into 3 pieces.

y = (effects to be adjusted for) + (effects to be kept) + residual

In a cross validation, you need a quantity “EBV + residual”, where EBV is (effects to be kept) shown above, and it is y_star. The program calculates the following quantities.

• y_star: y - (effects to be adjusted for)
• y_hat: (effects to kept)
• residual: residual

The user should specify the effects to be kept using OPTION include_effects with the effect numbers. For example, if the breeding value is in the 3rd row in the EFFECTS section in the parameter file, you have to use OPTION include_effects 3.

The main file is yhat_residual, which has corrected phenotypes and predicted residuals. The number of columns in this file depend on the number of traits (N).

• Column 1: Animal ID (renumbered i.e., same as the 1st column in renaddxx.ped)
• Column 2 to N+1: “y_star” explained above
• Column N+2 to 2N+1: “y_hat” explained above
• Column 2N+2 to 3N+1: “residual” explained above