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Fitting conditional and simultaneous autoregressive spatial models in hglm
Dalarna University, School of Technology and Business Studies, Statistics.ORCID iD: 0000-0002-3183-3756
Dalarna University, School of Technology and Business Studies, Statistics.ORCID iD: 0000-0002-1057-5401
Karolinska Institutet.
2015 (English)In: The R Journal, ISSN 2073-4859, Vol. 7, no 2, 5-18 p.Article in journal (Refereed) Published
Abstract [en]

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 eteroscedastic, 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.

Place, publisher, year, edition, pages
2015. Vol. 7, no 2, 5-18 p.
National Category
Probability Theory and Statistics
Research subject
Complex Systems – Microdata Analysis, General Microdata Analysis - methods
URN: urn:nbn:se:du-19286ISI: 000368551800002OAI: diva2:852852
Available from: 2015-09-10 Created: 2015-09-10 Last updated: 2016-02-11Bibliographically approved

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Alam, MoududRönnegård, Lars
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