A variant of sparse partial least squares for variable selection and data exploration

Megan J.Olson Hunt, Lisa Weissfeld, Robert M. Boudreau, Howard Aizenstein, Anne B. Newman, Eleanor M. Simonsick, Dane R. Van Domelen, Fridtjof Thomas, Kristine Yaffe, Caterina Rosano

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

When data are sparse and/or predictors multicollinear, current implementation of sparse partial least squares (SPLS) does not give estimates for non-selected predictors nor provide a measure of inference. In response, an approach termed "all-possible" SPLS is proposed, which fits a SPLS model for all tuning parameter values across a set grid. Noted is the percentage of time a given predictor is chosen, as well as the average non-zero parameter estimate. Using a "large" number of multicollinear predictors, simulation confirmed variables not associated with the outcome were least likely to be chosen as sparsity increased across the grid of tuning parameters, while the opposite was true for those strongly associated. Lastly, variables with a weak association were chosen more often than those with no association, but less often than those with a strong relationship to the outcome. Similarly, predictors most strongly related to the outcome had the largest average parameter estimate magnitude, followed by those with a weak relationship, followed by those with no relationship. Across two independent studies regarding the relationship between volumetric MRI measures and a cognitive test score, this method confirmed a priori hypotheses about which brain regions would be selected most often and have the largest average parameter estimates. In conclusion, the percentage of time a predictor is chosen is a useful measure for ordering the strength of the relationship between the independent and dependent variables, serving as a form of inference. The average parameter estimates give further insight regarding the direction and strength of association. As a result, all-possible SPLS gives more information than the dichotomous output of traditional SPLS, making it useful when undertaking data exploration and hypothesis generation for a large number of potential predictors.

Original languageEnglish (US)
Article number18
JournalFrontiers in Neuroinformatics
Volume8
Issue numberMAR
DOIs
StatePublished - Mar 3 2014

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Least-Squares Analysis
Tuning
Magnetic resonance imaging
Brain

All Science Journal Classification (ASJC) codes

  • Neuroscience (miscellaneous)
  • Biomedical Engineering
  • Computer Science Applications

Cite this

Hunt, M. J. O., Weissfeld, L., Boudreau, R. M., Aizenstein, H., Newman, A. B., Simonsick, E. M., ... Rosano, C. (2014). A variant of sparse partial least squares for variable selection and data exploration. Frontiers in Neuroinformatics, 8(MAR), [18]. https://doi.org/10.3389/fninf.2014.00018

A variant of sparse partial least squares for variable selection and data exploration. / Hunt, Megan J.Olson; Weissfeld, Lisa; Boudreau, Robert M.; Aizenstein, Howard; Newman, Anne B.; Simonsick, Eleanor M.; Van Domelen, Dane R.; Thomas, Fridtjof; Yaffe, Kristine; Rosano, Caterina.

In: Frontiers in Neuroinformatics, Vol. 8, No. MAR, 18, 03.03.2014.

Research output: Contribution to journalArticle

Hunt, MJO, Weissfeld, L, Boudreau, RM, Aizenstein, H, Newman, AB, Simonsick, EM, Van Domelen, DR, Thomas, F, Yaffe, K & Rosano, C 2014, 'A variant of sparse partial least squares for variable selection and data exploration', Frontiers in Neuroinformatics, vol. 8, no. MAR, 18. https://doi.org/10.3389/fninf.2014.00018
Hunt MJO, Weissfeld L, Boudreau RM, Aizenstein H, Newman AB, Simonsick EM et al. A variant of sparse partial least squares for variable selection and data exploration. Frontiers in Neuroinformatics. 2014 Mar 3;8(MAR). 18. https://doi.org/10.3389/fninf.2014.00018
Hunt, Megan J.Olson ; Weissfeld, Lisa ; Boudreau, Robert M. ; Aizenstein, Howard ; Newman, Anne B. ; Simonsick, Eleanor M. ; Van Domelen, Dane R. ; Thomas, Fridtjof ; Yaffe, Kristine ; Rosano, Caterina. / A variant of sparse partial least squares for variable selection and data exploration. In: Frontiers in Neuroinformatics. 2014 ; Vol. 8, No. MAR.
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