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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
@@ Line 3: / Line 3: @@
 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.