Authors / CoAuthors
Foster, S.D. | Shimadzu, H. | Darnell, R.
Abstract
In ecology, a common form of statistical analysis relates a biological variable to variables that delineate the physical environment, typically by fitting a regression model or one of its extensions. Unfortunately, the biological data and the physical data are frequently obtained from separate data sources. In such cases there is no guarantee that the biological and physical data are co-located and the regression model cannot be used. A common and pragmatic solution is to spatially predict the physical variables at the locations of the biological variables and then use the predictions as if they were observations. In this article, we show that this procedure can cause potentially misleading ferences when fitting a generalised linear model as an example. We propose a Berkson-error model which overcomes the limitations. The differences between using predicted covariates and the Berkson error model are illustrated using data from the marine environment, and a simulation study based on this data.
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nonGeographicDataset
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71580
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- External PublicationScientific Journal Paper
- Australian and New Zealand Standard Research Classification (ANZSRC)
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- Earth Sciences
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- Published_Internal
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2011-02-28T00:00:00
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