Model selection for inferential models with high dimensional data: synthesis and graphical representation of multiple techniques

Abstract Inferential research commonly involves identification of causal factors from within high dimensional data but selection of the ‘correct’ variables can be problematic. One specific problem is that results vary depending on statistical method employed and it has been argued that triangulation...

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Autores principales:	Eliana Lima, Robert Hyde, Martin Green
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/23cf382d4b0a43df854972ac27e9f3d6
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id	oai:doaj.org-article:23cf382d4b0a43df854972ac27e9f3d6
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spelling	oai:doaj.org-article:23cf382d4b0a43df854972ac27e9f3d62021-12-02T14:12:09ZModel selection for inferential models with high dimensional data: synthesis and graphical representation of multiple techniques10.1038/s41598-020-79317-82045-2322https://doaj.org/article/23cf382d4b0a43df854972ac27e9f3d62021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79317-8https://doaj.org/toc/2045-2322Abstract Inferential research commonly involves identification of causal factors from within high dimensional data but selection of the ‘correct’ variables can be problematic. One specific problem is that results vary depending on statistical method employed and it has been argued that triangulation of multiple methods is advantageous to safely identify the correct, important variables. To date, no formal method of triangulation has been reported that incorporates both model stability and coefficient estimates; in this paper we develop an adaptable, straightforward method to achieve this. Six methods of variable selection were evaluated using simulated datasets of different dimensions with known underlying relationships. We used a bootstrap methodology to combine stability matrices across methods and estimate aggregated coefficient distributions. Novel graphical approaches provided a transparent route to visualise and compare results between methods. The proposed aggregated method provides a flexible route to formally triangulate results across any chosen number of variable selection methods and provides a combined result that incorporates uncertainty arising from between-method variability. In these simulated datasets, the combined method generally performed as well or better than the individual methods, with low error rates and clearer demarcation of the true causal variables than for the individual methods.Eliana LimaRobert HydeMartin GreenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Eliana Lima Robert Hyde Martin Green Model selection for inferential models with high dimensional data: synthesis and graphical representation of multiple techniques
description	Abstract Inferential research commonly involves identification of causal factors from within high dimensional data but selection of the ‘correct’ variables can be problematic. One specific problem is that results vary depending on statistical method employed and it has been argued that triangulation of multiple methods is advantageous to safely identify the correct, important variables. To date, no formal method of triangulation has been reported that incorporates both model stability and coefficient estimates; in this paper we develop an adaptable, straightforward method to achieve this. Six methods of variable selection were evaluated using simulated datasets of different dimensions with known underlying relationships. We used a bootstrap methodology to combine stability matrices across methods and estimate aggregated coefficient distributions. Novel graphical approaches provided a transparent route to visualise and compare results between methods. The proposed aggregated method provides a flexible route to formally triangulate results across any chosen number of variable selection methods and provides a combined result that incorporates uncertainty arising from between-method variability. In these simulated datasets, the combined method generally performed as well or better than the individual methods, with low error rates and clearer demarcation of the true causal variables than for the individual methods.
format	article
author	Eliana Lima Robert Hyde Martin Green
author_facet	Eliana Lima Robert Hyde Martin Green
author_sort	Eliana Lima
title	Model selection for inferential models with high dimensional data: synthesis and graphical representation of multiple techniques
title_short	Model selection for inferential models with high dimensional data: synthesis and graphical representation of multiple techniques
title_full	Model selection for inferential models with high dimensional data: synthesis and graphical representation of multiple techniques
title_fullStr	Model selection for inferential models with high dimensional data: synthesis and graphical representation of multiple techniques
title_full_unstemmed	Model selection for inferential models with high dimensional data: synthesis and graphical representation of multiple techniques
title_sort	model selection for inferential models with high dimensional data: synthesis and graphical representation of multiple techniques
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/23cf382d4b0a43df854972ac27e9f3d6
work_keys_str_mv	AT elianalima modelselectionforinferentialmodelswithhighdimensionaldatasynthesisandgraphicalrepresentationofmultipletechniques AT roberthyde modelselectionforinferentialmodelswithhighdimensionaldatasynthesisandgraphicalrepresentationofmultipletechniques AT martingreen modelselectionforinferentialmodelswithhighdimensionaldatasynthesisandgraphicalrepresentationofmultipletechniques
_version_	1718391774340186112

Model selection for inferential models with high dimensional data: synthesis and graphical representation of multiple techniques

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