Abstract
The objective of this study was to evaluate the use of probit and logit link functions for the genetic evaluation of early pregnancy using simulated data. The following simulation/analysis structures were constructed: logit/logit, logit/probit, probit/logit, and probit/probit. The percentages of precocious females were 5, 10, 15, 20, 25 and 30% and were adjusted based on a change in the mean of the latent variable. The parametric heritability (h 2) was 0.40. Simulation and genetic evaluation were implemented in the R software. Heritability estimates (ĥ 2) were compared with h 2 using the mean squared error. Pearson correlations between predicted and true breeding values and the percentage of coincidence between true and predicted ranking, considering the 10% of bulls with the highest breeding values (TOP10) were calculated. The mean ĥ 2 values were underand overestimated for all percentages of precocious females when logit/probit and probit/logit models used. In addition, the mean squared errors of these models were high when compared with those obtained with the probit/probit and logit/logit models. Considering ĥ 2, probit/probit and logit/logit were also superior to logit/probit and probit/logit, providing values close to the parametric heritability. Logit/probit and probit/logit presented low Pearson correlations, whereas the correlations obtained with probit/probit and logit/logit ranged from moderate to high. With respect to the TOP10 bulls, logit/probit and probit/logit presented much lower percentages than probit/probit and logit/logit. The genetic parameter estimates and predictions of breeding values of the animals obtained with the logit/logit and probit/probit models were similar. In contrast, the results obtained with probit/logit and logit/probit were not satisfactory. There is need to compare the estimation and prediction ability of logit and probit link functions. © 2012 Sociedade Brasileira de Zootecnia.
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Garcia, D. A., Pereira, I. G., Silva, F. F., Rosa, G. J. de M., Pires, A. V., & Leandro, R. A. (2012). Generalized linear mixed models for the genetic evaluation of binary reproductive traits: A simulation study. Revista Brasileira de Zootecnia, 41(1), 52–57. https://doi.org/10.1590/S1516-35982012000100008
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