Abstract

We present a new version (>= 2.0) of the hglm package for fitting hierarchical generalized linear models (HGLMs) with spatially correlated random effects. CAR() and SAR() families for conditional and simultaneous autoregressive random effects were implemented. Eigen decomposition of the matrix describing the spatial structure (e.g., the neighborhood matrix) was used to transform the CAR/SAR random effects into an independent, but heteroscedastic, Gaussian random effect. A linear predictor is fitted for the random effect variance to estimate the parameters in the CAR and SAR models. This gives a computationally efficient algorithm for moderately sized problems.

Published in

The R journal
2015, Volume: 7, number: 2, pages: 5-18
Publisher: R FOUNDATION STATISTICAL COMPUTING

SLU Authors

• Rönnegård, Lars

  • Department of Animal Biosciences, Swedish University of Agricultural Sciences
    
  • Department of Clinical Sciences, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Probability Theory and Statistics


Permanent link to this page (URI)

https://res.slu.se/id/publ/77180