Formulating State Space Models in R with Focus on Longitudinal Regression Models

We provide a language for formulating a range of state space models with response densities within the exponential family. The described methodology is implemented in the R-package sspir. A state space model is specified similarly to a generalized linear model in R, and then the time-varying terms are marked in the formula. Special functions for specifying polynomial time trends, harmonic seasonal patterns, unstructured seasonal patterns and time-varying covariates can be used in the formula. The model is fitted to data using iterated extended Kalman filtering, but the formulation of models does not depend on the implemented method of inference. The package is demonstrated on three datasets.

Dynamic production monitoring in pig herds II:Modeling and monitoring farrowing rate at herd level

Abstract:Good management in animal production systems is becoming of paramount importance. The aim of this paper was to develop a dynamic moni-toring system for farrowing rate. A farrowing rate model was implemented us-ing a Dynamic Generalized Linear Model (DGLM). Variance components were pre-estimated using an Expectation-Maximization (EM) algorithm applied on a dataset containing data from 15 herds, each of them including insemination and farrowing observations over a period ranging from 150 to 800 weeks. The model included a set of parameters describing the parity-specific farrowing rate and the re-insemination effect. It also provided reliable forecasting on weekly basis. Sta-tistical control tools were used to give warnings in case of impaired farrowing rate. For each herd, farrowing rate profile, analysis of model components over time and detection of alarms were computed. Together with a previous model for litter size data and a planned similar model for mortality rate, this model will be an important basis for developing a new, dynamic, management tool