Consecuencias de la estructura espacial de los datos en el diseño y análisis de experimentos en campo
[EN] Many traits assessed in field ecological trials show nonrandom spatial structures that may affect the efficiency of standard statistical analyses. Although several more or less sophisticated experimental designs may improve this efficiency by controlling the spatial variation, there are many si...
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2006 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/45589 |
| Acceso en línea: | http://hdl.handle.net/10261/45589 |
| Access Level: | acceso abierto |
| Palabra clave: | Autocorrelación espacial Análisis espacial Geoestadística Semivariogramas Spatial autocorrelation Spatial analysis Geostatistics Semivariograms Kriging |
| Sumario: | [EN] Many traits assessed in field ecological trials show nonrandom spatial structures that may affect the efficiency of standard statistical analyses. Although several more or less sophisticated experimental designs may improve this efficiency by controlling the spatial variation, there are many situations where designs can not be properly arranged to actual spatial patterns. In such cases, spatial analysis techniques become essential to correctly analyse spatial autocorrelated data. In this paper, the effects of spatial autocorrelation on the results of conventional statistical analysis are discussed, and a spatial adjustment procedure, based on geostatistics, is proposed to be used when data are spatially autocorrelated. A case study is presented to show how conventional analysis of spatially autocorrelated data may give completely erroneous conclusions.Many traits assessed in field ecological trials show nonrandom spatial structures that may affect the efficiency of standard statistical analyses. Although several more or less sophisticated experimental designs may improve this efficiency by controlling the spatial variation, there are many situations where designs can not be properly arranged to actual spatial patterns. In such cases, spatial analysis techniques become essential to correctly analyse spatial autocorrelated data. In this paper, the effects of spatial autocorrelation on the results of conventional statistical analysis are discussed, and a spatial adjustment procedure, based on geostatistics, is proposed to be used when data are spatially autocorrelated. A case study is presented to show how conventional analysis of spatially autocorrelated data may give completely erroneous conclusions. |
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