Improving Practices for Selecting a Subset of Important Predictors in Psychology: An Application to Predicting Pain

Frequently, researchers in psychology are faced with the challenge of narrowing down a large set of predictors to a smaller subset. There are a variety of ways to do this, but commonly it is done by choosing predictors with the strongest bivariate correlations with the outcome. However, when predictors are correlated, bivariate relationships may not translate into multivariate relationships. Further, any attempts to control for multiple testing are likely to result in extremely low power. Here we introduce a Bayesian variable-selection procedure frequently used in other disciplines, stochastic search variable selection (SSVS). We apply this technique to choosing the best set of predictors of the perceived unpleasantness of an experimental pain stimulus from among a large group of sociocultural, psychological, and neurobiological (functional MRI) individual-difference measures. Using SSVS provides information about which variables predict the outcome, controlling for uncertainty in the other variables of the model. This approach yields new, useful information to guide the choice of relevant predictors. We have provided Web-based open-source software for performing SSVS and visualizing the results.

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A Bayesian variable selection procedure to rank overlapping gene sets

BMC Bioinformatics ◽

10.1186/1471-2105-13-73 ◽

2012 ◽

Vol 13 (1) ◽

pp. 73 ◽

Cited By ~ 5

Author(s):

Axel Skarman ◽

Mohammad Shariati ◽

Luc Jans ◽

Li Jiang ◽

Peter Sørensen

Keyword(s):

Variable Selection ◽

Selection Procedure ◽

Bayesian Variable Selection ◽

Overlapping Gene ◽

Gene Sets ◽

Variable Selection Procedure

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Improving Practices for Selecting a Subset of Important Predictors in Psychology: An Application to Predicting Pain

10.31234/osf.io/j8t7s ◽

2019 ◽

Author(s):

Sierra Bainter ◽

Thomas Granville McCauley ◽

Tor D Wager ◽

Elizabeth Reynolds Losin

Keyword(s):

Variable Selection ◽

Bayesian Variable Selection ◽

Limited Information ◽

Online Application ◽

Stochastic Search Variable Selection ◽

Selection Approach ◽

Pain Ratings ◽

Research Questions ◽

Standard Techniques ◽

Search Variable

In this paper we address the problem of selecting important predictors from some larger set of candidate predictors. Standard techniques are limited by lack of power and high false positive rates. A Bayesian variable selection approach used widely in biostatistics, stochastic search variable selection, can be used instead to combat these issues by accounting for uncertainty in the other predictors of the model. In this paper we present Bayesian variable selection to aid researchers facing this common scenario, along with an online application (https://ssvsforpsych.shinyapps.io/ssvsforpsych/) to perform the analysis and visualize the results. Using an application to predict pain ratings, we demonstrate how this approach quickly identifies reliable predictors, even when the set of possible predictors is larger than the sample size. This technique is widely applicable to research questions that may be relatively data-rich, but with limited information or theory to guide variable selection.

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A pseudo knockoff filter for correlated features

Information and Inference A Journal of the IMA ◽

10.1093/imaiai/iay012 ◽

2018 ◽

Vol 8 (2) ◽

pp. 313-341

Author(s):

Jiajie Chen ◽

Anthony Hou ◽

Thomas Y Hou

Keyword(s):

Variable Selection ◽

False Discovery Rate ◽

Numerical Experiments ◽

Selection Procedure ◽

Numerical Examples ◽

False Discovery ◽

Variable Selection Procedure ◽

Partial Analysis ◽

False Discovery Proportion

Abstract In Barber & Candès (2015, Ann. Statist., 43, 2055–2085), the authors introduced a new variable selection procedure called the knockoff filter to control the false discovery rate (FDR) and proved that this method achieves exact FDR control. Inspired by the work by Barber & Candès (2015, Ann. Statist., 43, 2055–2085), we propose a pseudo knockoff filter that inherits some advantages of the original knockoff filter and has more flexibility in constructing its knockoff matrix. Moreover, we perform a number of numerical experiments that seem to suggest that the pseudo knockoff filter with the half Lasso statistic has FDR control and offers more power than the original knockoff filter with the Lasso Path or the half Lasso statistic for the numerical examples that we consider in this paper. Although we cannot establish rigourous FDR control for the pseudo knockoff filter, we provide some partial analysis of the pseudo knockoff filter with the half Lasso statistic and establish a uniform false discovery proportion bound and an expectation inequality.

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Variable Selection Procedure for Discrimination Between two Multinormal Populations with Common Dispersion Matrix Proportional to a Known Positive Definite Matrix

Calcutta Statistical Association Bulletin ◽

10.1177/0008068320100301 ◽

2010 ◽

Vol 62 (3-4) ◽

pp. 129-142

Author(s):

Sisir Kumar Samanta

Keyword(s):

Variable Selection ◽

Selection Procedure ◽

Positive Definite Matrix ◽

Positive Definite ◽

Dispersion Matrix ◽

Variable Selection Procedure

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Genetic analysis of complex traits via Bayesian variable selection: the utility of a mixture of uniform priors

Genetics Research ◽

10.1017/s0016672311000164 ◽

2011 ◽

Vol 93 (4) ◽

pp. 303-318 ◽

Cited By ~ 12

Author(s):

TIMO KNÜRR ◽

ESA LÄÄRÄ ◽

MIKKO J. SILLANPÄÄ

Keyword(s):

Genetic Analysis ◽

Variable Selection ◽

Complex Traits ◽

Bayesian Variable Selection ◽

Stochastic Search Variable Selection ◽

Indicator Variables ◽

Supplementary Material ◽

Selection Of ◽

Made In ◽

Search Variable

SummaryA new estimation-based Bayesian variable selection approach is presented for genetic analysis of complex traits based on linear or logistic regression. By assigning a mixture of uniform priors (MU) to genetic effects, the approach provides an intuitive way of specifying hyperparameters controlling the selection of multiple influential loci. It aims at avoiding the difficulty of interpreting assumptions made in the specifications of priors. The method is compared in two real datasets with two other approaches, stochastic search variable selection (SSVS) and a re-formulation of Bayes B utilizing indicator variables and adaptive Student's t-distributions (IAt). The Markov Chain Monte Carlo (MCMC) sampling performance of the three methods is evaluated using the publicly available software OpenBUGS (model scripts are provided in the Supplementary material). The sensitivity of MU to the specification of hyperparameters is assessed in one of the data examples.

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