A Methodology for Biplots Based on Bootstrapping with R

• ANA B. NIETO ^[1] ; M. PURIFICACIÓN GALINDO ^[1] ; VÍCTOR LEIVA ^[2] ; PURIFICACIÓN VICENTE-GALINDO ^[1]
  1. [1] Universidad de Salamanca

     Universidad de Salamanca

     Salamanca, España

     
  2. [2] Universidad Adolfo Ibáñez

     Universidad Adolfo Ibáñez

     Santiago, Chile

     
• Localización: Revista Colombiana de Estadística, ISSN-e 2389-8976, ISSN 0120-1751, Vol. 37, Nº. 2, 2014, págs. 367-397
• Idioma: inglés
• DOI: 10.15446/rce.v37n2spe.47944
• Títulos paralelos:
  • Una metodología para biplots basada en bootstrapping con R
• Enlaces
  • Texto completo
• Resumen
  • español

    Un biplot es una representación gráfica de datos multivariantes de dos vías basada en marcadores para filas y columnas proporcionada usualmente en un espacio bidimensional. Estos marcadores definen parámetros que ayudan a interpretar bondad de ajuste, calidad de representación y variabilidad y relaciones entre variables. Sin embargo, tales parámetros son estimados puntualmente en el biplot, sin proporcionar información acerca de la precisión de los estimadores. Se propone una metodología gráfica, que puede ser considerada como una versión inferencial de un biplot, basada en intervalos de confianza bootstrap para los parámetros mencionados. La metodología es implementada en un paquete \verb"R" y validada con datos simulados y reales.

  • English

    A biplot is a graphical representation of two-mode multivariate data based on markers for rows and columns often provided in a two-dimensional space. These markers define parameters that help to interpret goodness of fit, quality of the representation and variability and relationships between variables. However, such parameters are estimated as point values by the biplot, thus no information on the accuracy of the corresponding estimators is obtained. We propose a graphical methodology, that may be considered as an inferential version of a biplot, based on bootstrap confidence intervals for the mentioned parameters. We implement our methodology in an \verb"R" package and validate it with simulated and real-world data.

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