Literature DB >> 35706709

Extension of biplot methodology to multivariate regression analysis.

Opeoluwa F Oyedele1.   

Abstract

At the core of multivariate statistics is the investigation of relationships between different sets of variables. More precisely, the inter-variable relationships and the causal relationships. The latter is a regression problem, where one set of variables is referred to as the response variables and the other set of variables as the predictor variables. In this situation, the effect of the predictors on the response variables is revealed through the regression coefficients. Results from the resulting regression analysis can be viewed graphically using the biplot. The consequential biplot provides a single graphical representation of the samples together with the predictor variables and response variables. In addition, their effect in terms of the regression coefficients can be visualized, although sub-optimally, in the said biplot.
© 2020 Informa UK Limited, trading as Taylor & Francis Group.

Entities:  

Keywords:  Biplot; multivariate regression; rank approximation; regression analysis

Year:  2020        PMID: 35706709      PMCID: PMC9042030          DOI: 10.1080/02664763.2020.1779192

Source DB:  PubMed          Journal:  J Appl Stat        ISSN: 0266-4763            Impact factor:   1.416


  1 in total

1.  Calibration of multivariate scatter plots for exploratory analysis of relations within and between sets of variables in genomic research.

Authors:  Jan Graffelman; Fred van Eeuwijk
Journal:  Biom J       Date:  2005-12       Impact factor: 2.207

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.