Open Access

A simulation study for the analysis of uncertain binary responses: Application to first insemination success in beef cattle

  • Robyn L. Sapp1,
  • Matthew L. Spangler1,
  • Romdhane Rekaya1, 2Email author and
  • J. Keith Bertrand1
Contributed equally
Genetics Selection Evolution200537:615

https://doi.org/10.1186/1297-9686-37-7-615

Received: 5 July 2004

Accepted: 6 August 2005

Published: 15 November 2005

Abstract

A simulation was carried out to investigate the methods of analyzing uncertain binary responses for success or failure at first insemination. A linear mixed model that included, herd, year, and month of mating as fixed effects; and unrelated service sire, sire and residual as random effects was used to generate binary data. Binary responses were assigned using the difference between days to calving and average gestation length. Females deviating from average gestation length lead to uncertain binary responses. Thus, the methods investigated were the following: (1) a threshold model fitted to certain (no uncertainty) binary data (M1); (2) a threshold model fitted to uncertain binary data ignoring uncertainty (M2); and (3) analysis of uncertain binary data, accounting for uncertainty from day 16 to 26 (M3) or from day 14 to 28 (M4) after introduction of the bull, using a threshold model with fuzzy logic classification. There was virtually no difference between point estimates obtained from M1, M3, and M4 with true values. When uncertain binary data were analyzed ignoring uncertainty (M2), sire variance and heritability were underestimated by 22 and 24%, respectively. Thus, for noisy binary data, a threshold model contemplating uncertainty is needed to avoid bias when estimating genetic parameters.

Keywords

binary data fertility fuzzy logic simulation threshold model

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Notes

Authors’ Affiliations

(1)
Animal and Dairy Science Department, The University of Georgia
(2)
Department of Statistics, The University of Georgia

Copyright

© INRA, EDP Sciences 2005

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