In this paper, we propose a generalization of Partial Least Squares Regression (PLS-R) for a matrix of several binary responses and a a set of numerical predictors. We call the method Partial Least Squares Binary Logistic Regression (PLS-BLR). That is equivalent to a PLS-2 model for binary responses. Biplot and even triplot graphical representations for visualizing PLS-BLR models are described, and an application to real data is presented. Software packages for the calculation of the main results are also provided. We conclude that the proposed method and its visualization using triplots are powerful tools for the interpretation of the relations among predictors and responses.

Classical Partial Least Squares Regression (PLSR) models were developed primarily for continuous data, allowing dimensionality reduction while preserving relationships between predictors and responses. However, their application to binary data is limited. This study introduces Binary Partial Least Squares Regression (BPLSR), a novel extension of the PLSR methodology designed specifically for scenarios involving binary predictors and responses. BPLSR adapts the classical PLSR framework to handle the unique properties of binary datasets. A key feature of this approach is the introduction of a triplot representation that integrates logistic biplots. This visualization tool provides an intuitive interpretation of relationships between individuals and variables from both predictor and response matrices, enhancing the interpretability of binary data analysis. To illustrate the applicability and effectiveness of BPLSR, the method was applied to a real-world dataset of strains of Colletotrichum graminicola, a pathogenic fungus. The results demonstrated the ability of the method to represent binary relationships between predictors and responses, underscoring its potential as a robust analytical tool. This work extends the capabilities of traditional PLSR methods and provides a practical and versatile solution for binary data analysis with broad applications in diverse research areas.

This article presents biplots derived from factor analysis of correlation matrices for both continuous and ordinal data. It introduces biplots specifically designed for factor analysis, detailing the geometric interpretation for each data type and providing an algorithm to compute biplot coordinates from the factorization of correlation matrices. The theoretical developments are illustrated using a real dataset that explores the relationship between volunteering, political ideology, and civic engagement in Spain.

El TDAH, la presencia de apego inseguro y los eventos traumáticos son habituales en menores en riesgo psicosocial atendidos en Programas de Apoyo a Familias (PAF) en servicios sociales. La similitud de estas variables con síntomas presentes en clasificaciones DSM-5 y CIE-11 en apartados como Trastornos relacionados con Traumas y Factores de Estrés, nos hace pensar que pueden inducir a confusión. El objetivo de la investigación es describir el tipo de apego y experiencias traumáticas vividas en adolescentes entre 13 y 17 años con/sin TDAH incluidos/as en PAF. El diseño de la investigación es el de un estudio empírico, que en función de los diferentes objetivos, tiene un diseño descriptivo y/o analítico observacional de corte transversal. Resultados: los/as menores con TDAH tienen una mayor proporción de apego inseguro (fundamentalmente ambivalente) y mayor rango promedio en el nº de eventos traumáticos que los/as menores sin TDAH. El grupo TDAH tiene mayor proporción de abuso/maltrato psicológico, abuso/maltrato físico y negligencia emocional que en el grupo sin TDAH. Respecto a las dimensiones relacionales y mediante análisis multivariable, se observa que el incremento de nº de eventos traumáticos de los/as menores influyen significativamente en la probabilidad de apego inseguro y de tener TDAH. Conclusiones: dado que el diagnóstico de TDAH es clínico y ajustado a DSM-5 o CIE-11 se debe realizar un buen diagnóstico diferencial y/o comorbilidad con trastornos de apego, trastornos por trauma y factores de estrés, maltrato y abuso.

This article describes biplots based on factor analysis on correlation matrices for continuous and ordinal data. This paper describes biplots specifically developed for factor analysis and the geometry for each type of data together with an algorithm to obtain biplot coordinates from the factorization of the correlation matrices. The main theoretical results are applied to a real data set about the relation between volunteering and political ideology and compromise in Spain.

Objectives: Gastric cancer is one of the leading causes of cancer-related mortality. Sarcopenia, characterized by reduced skeletal muscle mass, strength, and physical performance, is associated with poorer outcomes in gastrointestinal cancers. This study aimed to evaluate the prevalence of sarcopenia, including the Total Psoas Area Index (TPAI), and its association with tumor stage, postoperative complications, and survival in patients with gastric adenocarcinoma. Methods: A retrospective study was conducted, including 79 patients who underwent surgery for gastric adenocarcinoma. Sarcopenia was assessed using a combination of CT-derived muscle mass (TPAI), muscle strength (dynamometry), and functional status (SARC-F). Patients were divided into sarcopenic (SG) and non-sarcopenic (NSG) groups. Clinical and survival data were compared between groups. Results: Sarcopenia was identified in 36.7% of patients. Early-stage disease (I–II) was less frequent in the SG (20.7%) compared to the NSG (44%), whereas stage IV tumors were more common in the SG (20.7% vs. 6%). Sarcopenic patients had a lower body mass index and underwent fewer laparoscopic procedures (44.8% vs. 68%, p = 0.03). Postoperative complications, such as pneumonia and ileus, were more frequent in the SG. Overall survival was significantly lower in the SG (17 vs. 21 months, respectively, p = 0.042), with a reduction in disease-free survival as well. Postoperative TPAI values remained lower in the SG. Conclusions: Sarcopenia, as defined by combined assessment including TPAI, was associated with advanced tumor stage, increased postoperative risk, and reduced survival in patients with gastric cancer. Preoperative assessment of muscle mass may help identify high-risk patients and optimize clinical management.