Cargando…
Ultrahigh Dimensional Variable Selection for Interpolation of Point Referenced Spatial Data: A Digital Soil Mapping Case Study
Modern soil mapping is characterised by the need to interpolate point referenced (geostatistical) observations and the availability of large numbers of environmental characteristics for consideration as covariates to aid this interpolation. Modelling tasks of this nature also occur in other fields s...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5014409/ https://www.ncbi.nlm.nih.gov/pubmed/27603135 http://dx.doi.org/10.1371/journal.pone.0162489 |
_version_ | 1782452283995324416 |
---|---|
author | Fitzpatrick, Benjamin R. Lamb, David W. Mengersen, Kerrie |
author_facet | Fitzpatrick, Benjamin R. Lamb, David W. Mengersen, Kerrie |
author_sort | Fitzpatrick, Benjamin R. |
collection | PubMed |
description | Modern soil mapping is characterised by the need to interpolate point referenced (geostatistical) observations and the availability of large numbers of environmental characteristics for consideration as covariates to aid this interpolation. Modelling tasks of this nature also occur in other fields such as biogeography and environmental science. This analysis employs the Least Angle Regression (LAR) algorithm for fitting Least Absolute Shrinkage and Selection Operator (LASSO) penalized Multiple Linear Regressions models. This analysis demonstrates the efficiency of the LAR algorithm at selecting covariates to aid the interpolation of geostatistical soil carbon observations. Where an exhaustive search of the models that could be constructed from 800 potential covariate terms and 60 observations would be prohibitively demanding, LASSO variable selection is accomplished with trivial computational investment. |
format | Online Article Text |
id | pubmed-5014409 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-50144092016-09-27 Ultrahigh Dimensional Variable Selection for Interpolation of Point Referenced Spatial Data: A Digital Soil Mapping Case Study Fitzpatrick, Benjamin R. Lamb, David W. Mengersen, Kerrie PLoS One Research Article Modern soil mapping is characterised by the need to interpolate point referenced (geostatistical) observations and the availability of large numbers of environmental characteristics for consideration as covariates to aid this interpolation. Modelling tasks of this nature also occur in other fields such as biogeography and environmental science. This analysis employs the Least Angle Regression (LAR) algorithm for fitting Least Absolute Shrinkage and Selection Operator (LASSO) penalized Multiple Linear Regressions models. This analysis demonstrates the efficiency of the LAR algorithm at selecting covariates to aid the interpolation of geostatistical soil carbon observations. Where an exhaustive search of the models that could be constructed from 800 potential covariate terms and 60 observations would be prohibitively demanding, LASSO variable selection is accomplished with trivial computational investment. Public Library of Science 2016-09-07 /pmc/articles/PMC5014409/ /pubmed/27603135 http://dx.doi.org/10.1371/journal.pone.0162489 Text en © 2016 Fitzpatrick et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Fitzpatrick, Benjamin R. Lamb, David W. Mengersen, Kerrie Ultrahigh Dimensional Variable Selection for Interpolation of Point Referenced Spatial Data: A Digital Soil Mapping Case Study |
title | Ultrahigh Dimensional Variable Selection for Interpolation of Point Referenced Spatial Data: A Digital Soil Mapping Case Study |
title_full | Ultrahigh Dimensional Variable Selection for Interpolation of Point Referenced Spatial Data: A Digital Soil Mapping Case Study |
title_fullStr | Ultrahigh Dimensional Variable Selection for Interpolation of Point Referenced Spatial Data: A Digital Soil Mapping Case Study |
title_full_unstemmed | Ultrahigh Dimensional Variable Selection for Interpolation of Point Referenced Spatial Data: A Digital Soil Mapping Case Study |
title_short | Ultrahigh Dimensional Variable Selection for Interpolation of Point Referenced Spatial Data: A Digital Soil Mapping Case Study |
title_sort | ultrahigh dimensional variable selection for interpolation of point referenced spatial data: a digital soil mapping case study |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5014409/ https://www.ncbi.nlm.nih.gov/pubmed/27603135 http://dx.doi.org/10.1371/journal.pone.0162489 |
work_keys_str_mv | AT fitzpatrickbenjaminr ultrahighdimensionalvariableselectionforinterpolationofpointreferencedspatialdataadigitalsoilmappingcasestudy AT lambdavidw ultrahighdimensionalvariableselectionforinterpolationofpointreferencedspatialdataadigitalsoilmappingcasestudy AT mengersenkerrie ultrahighdimensionalvariableselectionforinterpolationofpointreferencedspatialdataadigitalsoilmappingcasestudy |