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On the goodness-of-fit of generalized linear geostatistical models

Research output: Contribution to journalJournal article

E-pub ahead of print
<mark>Journal publication date</mark>12/02/2018
<mark>Journal</mark>Spatial Statistics
<mark>State</mark>E-pub ahead of print
Early online date12/02/18
<mark>Original language</mark>English


We propose a generalization of Zhang’s coefficient of determination to generalized linear geostatistical models and illustrate its application to river-blindness mapping. The generalized coefficient of determination has a more intuitive interpretation than other measures of predictive performance and allows to assess the individual contribution of each explanatory variable and the random effects to spatial prediction. The developed methodology is also more widely applicable to any generalized linear mixed model.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Spatial Statistics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial Statistics, ??, 2018 DOI: 10.1016/j.spasta.2018.01.002