Analysis of social interactions in group-housed animals using dyadic linear models

Understanding factors affecting social interactions among animals is important for applied animal behavior research. Thus, there a need to elicit statistical models analyze data collected from pairwise behavioral interactions. In this study, we propose treating interaction as dyadic observations and model their analysis. We performed posterior predictive checks of the through different validation strategies: stratified 5-fold random cross-validation, block-by-social-group block-by-focal-animals validation. The proposed was pig dataset 797 growing pigs freshly remixed into 59 groups that resulted in 10,032 records directional response variable duration seconds each spent delivering attacks on another group mate. Generalized linear mixed were fitted. Fixed effects included sex, individual weight, prior nursery mate experience, littermate experience two dyad. Random aggressor, recipient, dyad, group. A Bayesian framework utilized parameter estimation checking. Prior only significant fixed effect. addition, weak but correlation between aggressor effect recipient obtained when analyzing attacking duration. performance varied depending strategy, with substantially lower strategy than other strategies. Collectively, paper demonstrates interactive behaviors, particularly