2013•Korean Journal of Veterinary ResearchOpen access

Application of varimax rotated principal component analysis in quantifying some zoometrical traits of a relict cow

Pere Miquel Parés-Casanova, I. Sinfreu, D. Villalba

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Abstract

A study was conducted to determine the interdependence among the conformation traits of 28 "Pallaresa" cows using principal component analysis.Originally 21 body linear measurements were obtained, from which eight traits are subsequently eliminated.From the principal components analysis, with raw varimax rotation of the transformation matrix, two principal components were extracted, which accounted for 65.8% of the total variance.The first principal component alone explained 51.6% of the variation, and tended to describe general size, while the second principal component had its loadings for back-sternal diameter.The two extracted principal components, which are traits related to dorsal heights and back-sternal diameter, could be considered in selection programs.

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A study was conducted to determine the interdependence among the conformation traits of 28 "Pallaresa" cows using principal component analysis.Originally 21 body linear measurements were obtained, from which eight traits are subsequently eliminated.From the principal components analysis, with raw varimax rotation of the transformation matrix, two principal components were extracted, which accounted for 65.8% of the total variance.The first principal component alone explained 51.6% of the variation, and tended to describe general size, while the second principal component had its loadings for back-sternal diameter.The two extracted principal components, which are traits related to dorsal heights and back-sternal diameter, could be considered in selection programs.

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Available abstract

A study was conducted to determine the interdependence among the conformation traits of 28 "Pallaresa" cows using principal component analysis.Originally 21 body linear measurements were obtained, from which eight traits are subsequently eliminated.From the principal components analysis, with raw varimax rotation of the transformation matrix, two principal components were extracted, which accounted for 65.8% of the total variance.The first principal component alone explained 51.6% of the variation, and tended to describe general size, while the second principal component had its loadings for back-sternal diameter.The two extracted principal components, which are traits related to dorsal heights and back-sternal diameter, could be considered in selection programs.

Key concepts: Varimax rotation, Principal component analysis, Mathematics, Statistics, Rotation (mathematics), Selection (genetic algorithm), Geometry, Computer science

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