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dc.creatorGili, Adriana Anahi-
dc.creatorNoellemeyer, Elke Johanna-
dc.creatorBalzarini, Monica Graciela-
dc.date2017-10-17T18:01:15Z-
dc.date2017-10-17T18:01:15Z-
dc.date2012-08-
dc.date2015-09-30T19:45:28Z-
dc.date.accessioned2019-04-29T15:32:06Z-
dc.date.available2019-04-29T15:32:06Z-
dc.date.issued2017-10-17T18:01:15Z-
dc.date.issued2017-10-17T18:01:15Z-
dc.date.issued2012-08-
dc.date.issued2015-09-30T19:45:28Z-
dc.identifierGili, Adriana Anahi; Noellemeyer, Elke Johanna; Balzarini, Monica Graciela; Hierarchical linear mixed models in multi-stage sampling soil studies; Springer; Environmental And Ecological Statistics; 20; 2; 8-2012; 237-252-
dc.identifier1352-8505-
dc.identifierhttp://hdl.handle.net/11336/26723-
dc.identifier1573-3009-
dc.identifierCONICET Digital-
dc.identifierCONICET-
dc.identifier.urihttp://rodna.bn.gov.ar:8080/jspui/handle/bnmm/295942-
dc.descriptionThe issue of variances of different soil variables prevailing at different sampling scales is addressed. This topic is relevant for soil science, agronomy and landscape ecology. In multi-stage sampling there are randomness components in each stage of sampling which can be taken into account by introducing random effects in analysis through the use of hierarchical linear mixed models (HLMM). Due to the nested sampling scheme, there are several hierarchical sub-models. The selection of the best model can be carried out through likelihood ratio tests (LRTs) or Wald tests, which are asymptotically equivalent under standard conditions. However, when the comparison leads to a restricted hypothesis of variance components, standard conditions are not maintained, which leads to more elaborated versions of LRTs. These versions are not disseminated among environmental scientists. The present study shows the modeling of soil data from a sampling where sites, fields within sites, transects within fields, and sampling points within transects were selected in order to take samples from different vegetation types (open and shade). For soil data, several sub-models were compared using Wald tests, classic LRTs and adjusted LRTs where the distribution of the test statistic under the null hypothesis is the Chi-square mixture of Chi-square distributions. The inclusion of random effects via HLMM and suggested by the latest version of LRT allowed us to detect effects of vegetation type on soil properties that were not detected under a classical ANOVA.-
dc.descriptionFil: Gili, Adriana Anahi. Universidad Nacional de La Pampa. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina-
dc.descriptionFil: Noellemeyer, Elke Johanna. Universidad Nacional de La Pampa. Facultad de Agronomía; Argentina-
dc.descriptionFil: Balzarini, Monica Graciela. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina-
dc.formatapplication/pdf-
dc.formatapplication/pdf-
dc.languageeng-
dc.publisherSpringer-
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1007/s10651-012-0217-0-
dc.relationinfo:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007%2Fs10651-012-0217-0-
dc.rightsinfo:eu-repo/semantics/restrictedAccess-
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/-
dc.sourcereponame:CONICET Digital (CONICET)-
dc.sourceinstname:Consejo Nacional de Investigaciones Científicas y Técnicas-
dc.sourceinstacron:CONICET-
dc.subjectBulk density-
dc.subjectLikelihood ratio test (LRT)-
dc.subjectTexture-
dc.subjectTotal organic carbon-
dc.subjectOtras Matemáticas-
dc.subjectMatemáticas-
dc.subjectCIENCIAS NATURALES Y EXACTAS-
dc.titleHierarchical linear mixed models in multi-stage sampling soil studies-
dc.typeinfo:eu-repo/semantics/article-
dc.typeinfo:eu-repo/semantics/publishedVersion-
dc.typeinfo:ar-repo/semantics/articulo-
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