Fitting spatial models in the R package: hglm
2014 (English)Report (Other academic)
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.
Working papers in transport, tourism, information technology and microdata analysis, ISSN 1650-5581 ; 2014:01
Spatial HGLM, Conditional autoregressive random effects model, Heteroskedastic random effects, Eigen decomposition
Probability Theory and Statistics
Research subject Complex Systems – Microdata Analysis, General Microdata Analysis - methods
IdentifiersURN: urn:nbn:se:du-13604OAI: oai:DiVA.org:du-13604DiVA: diva2:685966