INTRODUCTION Gait variables are highly correlated and have a temporal dependence [1]. Principal component analysis (PCA) has been applied successfully to extract information from highly correlated data [2]. Interpretation of the PCs’ is challenging. An alternative is to use a graphical display called Biplot [3] that gives insights into relationships and trends of complex data sets. Our goal was...

genotype×environment interactions (geis) can affect breeding programs because they often complicate the evaluation and selection of superior genotypes. this drawback can be reduced by gaining insights into gei processes and genotype adaptation. the objectives of this research were to evaluate: (1) the yield stability of promising wheat lines across locations and (2) the relationship among the t...

the ammi and sreg gge   are among the models that effectively capture the additive and multiplicative components of genotype × environment interaction (gei) and provide meaningful interpretation of multi-environment trials’ data set in the breeding programs. the objective of this study was to assess the effect of gei on grain yield of barely advanced lines and exploit the positive gei effect us...

Principal Component Analysis (PCA from now on) is a multivariate data analysis technique used for many different purposes and in many different contexts. PCA is the basis for low rank least squares approximation of a data matrix, for finding linear combinations with maximum or minimum variance, for fitting bilinear biplot models, for computing factor analysis approximations, and for studying re...

Genotype x environment interaction was evaluated under eight environments during lentil growing season of 2013/14 and 2014/15 for grain yield and their component characters of twenty one promising genotypes selected from previous trial of lentil. The variances estimated due genotype, environment and genotype x environment interaction were found to be different significantly for all the characte...

In this paper the methodology of biplots is introduced as a means for monitoring the behaviour of process systems. This sophisticated methodology allows for the projection of high-dimensional data to a low-dimensional subspace that can be visualised by a human operator. The projections are highly graphical in nature, and rich in information regarding variation in process variables, correlations...

Under the assumption that the Aitchison geometry holds in the simplex, standard analysis of compositional data assumes a uniform distribution as reference measure of the space. Changing the reference measure induces a weighting of parts. The changes that appear in the algebraic-geometric structure of the simplex are analysed, as a step towards understanding the implications for elementary stati...