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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
Identifiers
URN: urn:nbn:se:du-19286ISI: 000368551800002OAI: oai:DiVA.org:du-19286DiVA: diva2:852852
Available from: 2015-09-10 Created: 2015-09-10 Last updated: 2016-11-28Bibliographically approved

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fulltext(3363 kB)31 downloads
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Alam, MoududRönnegård, Lars
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf