2026-10-07T00:11:15Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33936784 2026-09-18T15:42:38Z category_27211 category_24550 category_29869 item_type_3 month_year_09_2026

Dataset and R Code: Elastic Net Regression for Mapping Anthropometric Predictors of Maximal Isometric Force Armando Monterrosa Quintero (19804209) Biomechanics Sports medicine Applied statistics Kinanthropometry Isometric Dynamometry penalized regression analysis <p dir="ltr">This repository contains the dataset and computational code supporting the research article:</p><p dir="ltr">"Regularized Elastic Net Regression for Mapping Anthropometric Predictors of Isometric Force in Physical Education Students".</p><p><br></p><p dir="ltr">Files included:</p><p dir="ltr">1. anthropometry_isometric_strength_dataset.xlsx (or .csv): </p><p dir="ltr"> Complete anonymized evaluation matrix comprising 44 young male participants (N = 44) evaluated across 44 kinanthropometric dimensions (ISAK full profile plus chronological age) and three maximal voluntary isometric strength tasks: Isometric Mid-Thigh Pull (IMTP), Pec Deck, and Leg Extension.</p><p><br></p><p dir="ltr">2. Kinanthropometry-Isometric-Strength-ElasticNet_code.R: </p><p dir="ltr"> Full reproducible computational pipeline developed in R (version 4.5.2). The script executes:</p><p dir="ltr"> - Automated data auditing and quality screening (validate / janitor / performance).</p><p dir="ltr"> - Regularized Elastic Net modeling (alpha = 0.5, 10-fold cross-validation).</p><p dir="ltr"> - Export of Table 1 and Table 2 in publication-ready formats (.docx).</p><p dir="ltr"> - Vector topological mapping onto a human anatomical silhouette (Figure 1 at 600 DPI).</p><p dir="ltr"> - Tuning profiles (Figure 2), coefficient paths (Figure 3), non-parametric bootstrap resampling (B = 1,000 iterations, Figure 4), and Bland–Altman agreement diagnostics (Figure 5).</p><p dir="ltr"> - Sensitivity ablation (IMTP without age) and Y-scrambling permutation testing (Leg Extension).</p><p><br></p><p dir="ltr">License: Creative Commons Attribution 4.0 International (CC BY 4.0).</p><p dir="ltr">Ethical Approval: CEBIC Universidad Santo Tomás, Seccional Tunja (Certificate code: DII-CI-2026-048).</p><p dir="ltr"><br></p> 2026-09-18T15:42:38Z Dataset Dataset 10.6084/m9.figshare.33936784.v1 https://figshare.com/articles/dataset/Dataset_and_R_Code_Elastic_Net_Regression_for_Mapping_Anthropometric_Predictors_of_Maximal_Isometric_Force/33936784 CC BY 4.0