Fitting Conditional and Simultaneous Autoregressive Spatial Models in hglm

Moudud Alam*, Lars Roennegard, Xia Shen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.

Original languageEnglish
Pages (from-to)5-18
Number of pages14
JournalThe R Journal
Issue number2
Publication statusPublished - 9 Sept 2015

Keywords / Materials (for Non-textual outputs)

  • INLA


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