Ultrahigh dimensional variable selection for interpolation of point referenced spatial data: A digital soil mapping case study

Benjamin R. Fitzpatrick; David W. Lamb; Kerrie Mengersen

doi:10.1371/journal.pone.0162489

Ultrahigh dimensional variable selection for interpolation of point referenced spatial data: A digital soil mapping case study

Benjamin R. Fitzpatrick, David W. Lamb, Kerrie Mengersen

Research output: Contribution to journal › Article › peer-review

10 Citations (Scopus)

Abstract

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.

Original language	English
Article number	e0162489
Number of pages	19
Journal	PLoS One
Volume	11
Issue number	9
DOIs	https://doi.org/10.1371/journal.pone.0162489
Publication status	Published - Sept 2016
Externally published	Yes

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10.1371/journal.pone.0162489Licence: CC BY

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abstract = "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.",

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Ultrahigh dimensional variable selection for interpolation of point referenced spatial data: A digital soil mapping case study

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