Data Inspection using Biplots

Abstract

Biplots display interunit distances, as well as variances and correlations of variables of large datasets. They can be used as a tool to reveal clustering, multicollinearity, and multivariate outliers, and to guide the interpretation of principal component analyses (PCA). This article describes the uses of biplots and its implementation in Stata.

Identification and characterization of potassium solubilizing bacteria (KSB) from Indo-Gangetic Plains of India

“…An angle of 0 or 180⁰ reflects a correlation of 1 or −1, respectively (Kohler and Luniak, 2005). The superimposition of KSB strains and K-solublization in various stressful growth environments showed that Pseudomonas sabulinigri BHU19 and Pseudomonas azotoformans BHU21 showed significantly higher correlation with all major parameters (Tables 8 and 9).…”

Relaciones crediticias y riesgo de contagio en el mercado interbancario no colateralizado colombiano

“…Finalmente, el análisis gráfico es sumamente útil para la interpretación del ACP. El biplot es un gráfico de k ejes (cada uno representa un componente principal) y que permiten detectar estructuras de clustering, multicolinealidad y datos atípicos a nivel multivariado en un conjunto de observaciones (Kohler & Luniak 2005). Por lo general, el biplot es una representación cartesiana de dos o tres dimensiones, es decir, se espera que dos o tres componentes expliquen en conjunto la mayor proporción de la variabilidad de los datos.…”

Investigating the technical sustainability of farming systems with correlational biplots

“…PCA biplots represent projections of multivariate datasets that exhibit the variance-covariance (or correlational) structure of the variables, the values of observations related to those variables, and the distances between observations in a multidimensional space for a given data matrix (Kohler and Luniak 2005). The PCs are arranged in the order of decreasing variance.…”