Assessment of Local Influence in the Fitting of Lactation Curves Using Nonlinear Mixed Models

Abstract

The identification of highly influential animals in the estimation of model parameters is fundamental to understanding individual variation in lactation patterns and to identifying unique profiles, helping the researcher to gain insight into the phenomenon under investigation. In this study, we analyzed the lactation curves of 30 Saanen goats using a nonlinear mixed-effects model derived from Wood's lactation model and applied a local influence diagnostic tool to detect animals that may disproportionately affect parameter estimates. The data comes from an unbalanced experiment designed to study the effect of cumulative stress on the average weekly milk production. The diagnostic of local influence enabled the identification of some animals with an unusual profile. Through the parameters of the fitted model, we identified that the treatment effect was not statistically significant during the experimental week, but negatively affected milk production throughout the lactation period.