Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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
Fitting spatial models in the R package: 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
Swedish University of Agricultural Sciences, Uppsala.
2014 (English)Report (Other academic)
Abstract [en]

We present a new version of the hglm package for fittinghierarchical generalized linear models (HGLM) with spatially correlated random effects. A CAR family for conditional autoregressive random effects was implemented. Eigen decomposition of the matrix describing the spatial structure (e.g. the neighborhood matrix) was used to transform the CAR random effectsinto an independent, but heteroscedastic, gaussian random effect. A linear predictor is fitted for the random effect variance to estimate the parameters in the CAR model.This gives a computationally efficient algorithm for moderately sized problems (e.g. n<5000).

Place, publisher, year, edition, pages
Högskolan Dalarna, 2014.
Series
Working papers in transport, tourism, information technology and microdata analysis, ISSN 1650-5581 ; 2014:01
Keyword [en]
Spatial HGLM, Conditional autoregressive random effects model, Heteroskedastic random effects, Eigen decomposition
National Category
Probability Theory and Statistics
Research subject
Complex Systems – Microdata Analysis, General Microdata Analysis - methods
Identifiers
URN: urn:nbn:se:du-13604OAI: oai:DiVA.org:du-13604DiVA: diva2:685966
Available from: 2014-01-10 Created: 2014-01-10 Last updated: 2015-12-11Bibliographically approved

Open Access in DiVA

fulltext(1097 kB)