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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.<\/jats:p>","DOI":"10.1177\/0962280213515792","type":"journal-article","created":{"date-parts":[[2013,12,24]],"date-time":"2013-12-24T21:20:33Z","timestamp":1387920033000},"page":"2138-2160","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Spatial generalised linear mixed models based on distances"],"prefix":"10.1177","volume":"25","author":[{"given":"Oscar O","family":"Melo","sequence":"first","affiliation":[{"name":"Department of Statistics, Faculty of Sciences, National University of Colombia, Bogot\u00e1, 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