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

Hunt, MJO and Weissfeld, L and Boudreau, RM and Aizenstein, H and Newman, AB and Simonsick, EM and Van Domelen, DR and Thomas, F and Yaffe, K and Rosano, C (2014) A variant of sparse partial least squares for variable selection and data exploration. Frontiers in Neuroinformatics, 8 (MAR). ISSN 1662-5196

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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. © 2014 Olson Hunt, Weissfeld, Boudreau, Aizenstein, Newman, Simonsick, Van Domelen, Thomas, Yaffeand Rosano.

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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Hunt, MJO
Weissfeld, L
Boudreau, RMrob21@pitt.eduROB21
Aizenstein, Haizen@pitt.eduAIZEN
Newman, ABANEWMAN@pitt.eduANEWMAN
Simonsick, EM
Van Domelen, DR
Thomas, F
Yaffe, K
Rosano, CRosanoC@edc.pitt.eduCAR2350
Date: 3 March 2014
Date Type: Publication
Journal or Publication Title: Frontiers in Neuroinformatics
Volume: 8
Number: MAR
DOI or Unique Handle: 10.3389/fninf.2014.00018
Schools and Programs: School of Public Health > Biostatistics
School of Public Health > Epidemiology
School of Medicine > Psychiatry
Refereed: Yes
ISSN: 1662-5196
Date Deposited: 05 May 2015 15:11
Last Modified: 02 Jul 2022 10:57
URI: http://d-scholarship.pitt.edu/id/eprint/24878

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