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