Ngspatial

A package for fitting the centered autologistic and sparse spatial generalized linear mixed models for areal data

John Hughes

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Two important recent advances in areal modeling are the centered autologistic model and the sparse spatial generalized linear mixed model (SGLMM), both of which are reparameterizations of traditional models. The reparameterizations improve regression inference by alleviating spatial confounding, and the sparse SGLMM also greatly speeds computing by reducing the dimension of the spatial random effects. Package ngspatial ('ng' = non-Gaussian) provides routines for fitting these new models. The package supports composite likelihood and Bayesian inference for the centered autologistic model, and Bayesian inference for the sparse SGLMM.

Original languageEnglish (US)
Pages (from-to)81-95
Number of pages15
JournalR Journal
Volume6
Issue number2
StatePublished - Jan 1 2014

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Generalized Linear Mixed Model
Reparameterization
Bayesian inference
Composite Likelihood
Likelihood Inference
Confounding
Random Effects
Model
Regression
Generalized linear mixed model
Computing
Modeling
Composite materials

Cite this

Ngspatial : A package for fitting the centered autologistic and sparse spatial generalized linear mixed models for areal data. / Hughes, John.

In: R Journal, Vol. 6, No. 2, 01.01.2014, p. 81-95.

Research output: Contribution to journalArticle

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