A variant of sparse partial least squares for variable selection and data exploration
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 al...
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| Format: | Online |
| Language: | English |
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Frontiers Media S.A.
2014
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3939647/ |
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pubmed-3939647 |
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pubmed-39396472014-03-12 A variant of sparse partial least squares for variable selection and data exploration Olson Hunt, Megan J. Weissfeld, Lisa Boudreau, Robert M. Aizenstein, Howard Newman, Anne B. Simonsick, Eleanor M. Van Domelen, Dane R. Thomas, Fridtjof Yaffe, Kristine Rosano, Caterina Neuroscience 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. Frontiers Media S.A. 2014-03-03 /pmc/articles/PMC3939647/ /pubmed/24624079 http://dx.doi.org/10.3389/fninf.2014.00018 Text en Copyright © 2014 Olson Hunt, Weissfeld, Boudreau, Aizenstein, Newman, Simonsick, Van Domelen, Thomas, Yaffe and Rosano. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Olson Hunt, Megan J. Weissfeld, Lisa Boudreau, Robert M. Aizenstein, Howard Newman, Anne B. Simonsick, Eleanor M. Van Domelen, Dane R. Thomas, Fridtjof Yaffe, Kristine Rosano, Caterina |
| spellingShingle |
Olson Hunt, Megan J. 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 |
| author_facet |
Olson Hunt, Megan J. Weissfeld, Lisa Boudreau, Robert M. Aizenstein, Howard Newman, Anne B. Simonsick, Eleanor M. Van Domelen, Dane R. Thomas, Fridtjof Yaffe, Kristine Rosano, Caterina |
| author_sort |
Olson Hunt, Megan J. |
| title |
A variant of sparse partial least squares for variable selection and data exploration |
| title_short |
A variant of sparse partial least squares for variable selection and data exploration |
| title_full |
A variant of sparse partial least squares for variable selection and data exploration |
| title_fullStr |
A variant of sparse partial least squares for variable selection and data exploration |
| title_full_unstemmed |
A variant of sparse partial least squares for variable selection and data exploration |
| title_sort |
variant of sparse partial least squares for variable selection and data exploration |
| description |
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. |
| publisher |
Frontiers Media S.A. |
| publishDate |
2014 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3939647/ |
| _version_ |
1612063424365199360 |