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covariance_structure_of_correlated_random_effects_for_multiple_traits

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covariance_structure_of_correlated_random_effects_for_multiple_traits [2018/03/29 18:55]
andres
covariance_structure_of_correlated_random_effects_for_multiple_traits [2018/04/09 16:13]
andres
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 For instance terminal crosses models (e.g. [[https://​gsejournal.biomedcentral.com/​articles/​10.1186/​s12711-016-0211-3||here]]) use: For instance terminal crosses models (e.g. [[https://​gsejournal.biomedcentral.com/​articles/​10.1186/​s12711-016-0211-3||here]]) use:
  
-  * trait_pure: **y = hys u + e** (animal)  +  * trait_pure: **y = herd-year-season + u + e** (animal)  
-  * trait_crossbred : **y = hys s + e** (sire of crossbred)+  * trait_crossbred : **y = herd-year-season + s + e** (sire of crossbred)
  
 Thus we have two traits, purebred and crossbred performance,​ but effect **u** only affects purebreds and effect **s** only affects crossbreds. Data file has "​animal"​ in column 4 and "​sire"​ in column 5. Effect **hys** has respectively 12 and 25 levels. ​ Thus we have two traits, purebred and crossbred performance,​ but effect **u** only affects purebreds and effect **s** only affects crossbreds. Data file has "​animal"​ in column 4 and "​sire"​ in column 5. Effect **hys** has respectively 12 and 25 levels. ​
covariance_structure_of_correlated_random_effects_for_multiple_traits.txt · Last modified: 2018/04/09 16:13 by andres