Spatial generalised linear mixed models based on distances
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http://dx.doi.org/10.1177/0962280213515792 |
Metadatos
Título
Spatial generalised linear mixed models based on distancesFecha de publicación
2016Editor
Sage PublicationsISSN
0962-2802; 1477-0334Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://journals.sagepub.com/doi/full/10.1177/0962280213515792Palabras clave / Materias
Resumen
Risk models derived from environmental data have been widely shown to be effective in delineating
geographical areas of risk because they are intuitively easy to understand. We present a new method
based on distances, ... [+]
Risk models derived from environmental data have been widely shown to be effective in delineating
geographical areas of risk because they are intuitively easy to understand. We present a new method
based on distances, which allows the modelling of continuous and non-continuous random variables
through distance-based spatial generalised linear mixed models. The parameters are estimated using
Markov chain Monte Carlo maximum likelihood, which is a feasible and a useful technique. The
proposed method depends on a detrending step built from continuous or categorical explanatory
variables, or a mixture among them, by using an appropriate Euclidean distance. The method is
illustrated through the analysis of the variation in the prevalence of Loa loa among a sample of village
residents in Cameroon, where the explanatory variables included elevation, together with maximum
normalised-difference vegetation index and the standard deviation of normalised-difference vegetation
index calculated from repeated satellite scans over time. [-]
Publicado en
Statistical Methods in Medical Research 2016, Vol. 25(5) 2138–2160Derechos de acceso
© The Author(s) 2013. Reprints and permissions: sagepub.co.uk/journalsPermissions.nav
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