Advantages of Using HJ-Biplot Analysis in Executive Functions Studies

Ruiz, Natalia Cadavid; Egido, Jaime; Galindo-Villardón, Purificación; Del-Río, Pablo

doi:10.1590/0102.3772e3426

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…In this study we used HJ-Biplot to visualize the data which has many advantages. (1) It allows the simultaneous examination of multiple variables in the same graphical representational plane (2) It allows to compare on an individual and group level (3) It identifies the provision of information on the relationship between measures of the same and/or different origin; (4) There is no need to perform a posteriori contrasts (Ruiz et al, 2018). However, it is difficult for the user to know which points (variables) are driving the solution and thus should be used to interpret the results.…”

Section: Compositional Data Analysis (Cda)mentioning

confidence: 99%

Preliminary assessment of heavy metals in surface water and sediment in Nakuvadra-Rakiraki River, Fiji using indexical and chemometric approaches

Kumar

Islam

Hasanuzzaman

et al. 2021

Journal of Environmental Management

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Section: Compositional Data Analysis (Cda)mentioning

confidence: 99%

Preliminary assessment of heavy metals in surface water and sediment in Nakuvadra-Rakiraki River, Fiji using indexical and chemometric approaches

Kumar

Islam

Hasanuzzaman

et al. 2021

Journal of Environmental Management

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“…A more exhaustive review of HJ-Biplot applications can be found in Nieto 2014 11 . Some recent applications of the HJ-Biplot method are found in Serafim 2013 10 ; Gallego-Álvarez 2015 12 ; Delgado-Álvarez 2015 13 ; Patino-Alonso 2015 14 ; Hernández-Suárez 2016 15 ; Ruiz 2018 16 ; Oliveira-Da Silva 2019 17 ; and Jaramillo-Feijoo 2020 18 .…”

Section: For Projections Of Row Markers Onto Column Markersmentioning

confidence: 99%

COVID-19 data analysis using HJ-Biplot method: A study case

Tenesaca-Chillogallo

Amaro

2021

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COVID-19 is a new viral disease declared as a pandemic by the World Health Organization in 2020. This highly infectious disease, caused by the virus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has shown a different impact on young individuals, old individuals, and people with other health conditions. In this study, we aim to identify the different relationships between COVID-19 with other health complications. Additionally, our purpose is to ordinate the ages of individuals according to COVID-19 affection. For this, we apply the HJ-Biplot along with Cluster multivariate analysis techniques to a data set of patients diagnosed with COVID-19, Pneumonia, smoking Habits, Diabetes, Obesity, Chronic Obstructive Pulmonary Disease, Asthma, Immunosuppression, Hypertension, Cardiovascular Problems, Chronic Renal Insufficiency, and other health problems. The data set that we use in this work was obtained from the Mexico Government website. This exploratory research illustrates that COVID-19 disease presents different relationships with other health problems and individuals. For example, this viral disease is highly correlated with Hypertension, Diabetes, smoking habits, and Obesity health conditions. Other high correlations are found among Chronic Obstructive Pulmonary Disease, Cardiovascular problems, Pneumonia, Chronic Renal Insufficiency, Hypertension, and Diabetes. Also, the ordination of individuals according to COVID-19 affection shows us that 60 to 89 years old people are highly affected by this disease. Furthermore, we identify the formation of two clusters of individuals. On the one hand, the first Cluster is formed by old individuals highly affected by many diseases. On the other hand, the second Cluster is formed mainly by young individuals lowly affected by this study's health problems. Identifying COVID-19 correlated health conditions and the age of the most affected individuals is crucial to the correct handle of the pandemic.

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“…Thus, its use has increased over the last 20 years in multiple fields (food industry, health, and social sciences) because HJ-Biplot provides options for evaluating associations between multiple observations and variables, with fewer restrictions than conventional methods, and the advantage of being specifically designed for the inspection of data matrices (Martínez-Regalado et al, 2021). Ruiz et al (2018) highlighted four main advantages: a simultaneous examination of multiple variables in the same graphical representational plane; different comparisons at individual and group levels; the relationship between measures of the same and/or different origins; and no need to perform a posteriori contrast.…”

Section: Introductionmentioning

confidence: 99%

Association of Dietary Intake of Polyphenols with an Adequate Nutritional Profile in Postpartum Women from Argentina

Miranda

Scotta

Cortez

et al. 2022

Prev. Nutr. Food Sci.

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HJ-Biplot analysis is a multivariate graphic representation that collects data covariation structure between variables and individuals to represent them in a low-dimensional space with the highest quality in the same reference system. Consequently, it is a promising technique for evaluating dietary exposure to polyphenols and accurately characterizing female nutrition. Herein, we hypothesized that polyphenol intake defines specific clusters with dietary impacts, which can be assessed using HJ-Biplot, based on a cross-sectional study in Argentina. The study included 275 healthy postpartum women who provided information about their food frequency intake and other conditions, which were then used to evaluate polyphenolic intake using the Phenol-Explorer database. Outcomes were established using HJ-Biplot for clustering and ANOVA to compare their impact on diet quality indicators. Two HJ-Biplot models were run (for intakes >20 mg/d and 5∼20 mg/d, respectively) to identify three clusters per model with excellent statistical fitness to explain the data. Thus, specific polyphenolic clusters with potentially bioactive and safe compounds were defined despite significant interindividual variability. In fact, women with the lowest polyphenolic intake exhibited worse dietary quality, body fat, and physical activity. As a result, HJ-Biplot proved to be an effective technique for clustering women based on their dietary intake of these compounds. Furthermore, cluster membership improved the intake of antioxidants, water, fiber, and healthy fats. Additionally, women with formal jobs and a higher educational level showed a better diet. Dietary polyphenols are critical during postpartum because they exert beneficial effects on women and breastfed infants.

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Advantages of Using HJ-Biplot Analysis in Executive Functions Studies

Cited by 5 publications

References 24 publications

Preliminary assessment of heavy metals in surface water and sediment in Nakuvadra-Rakiraki River, Fiji using indexical and chemometric approaches

Preliminary assessment of heavy metals in surface water and sediment in Nakuvadra-Rakiraki River, Fiji using indexical and chemometric approaches

COVID-19 data analysis using HJ-Biplot method: A study case

Association of Dietary Intake of Polyphenols with an Adequate Nutritional Profile in Postpartum Women from Argentina

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