1Department of Animal Science, Faculty of Veterinary, Van Yuzuncu Yil University, 6508, Van, Turkiye
2Department of Biostatistics, Faculty of Medicine, Van Yuzuncu Yil University, 6508, Van, Turkiye
3T.C. Ministry of Agriculture and Forestry, Ceylanpinar District Directorate of Agriculture, Sanliurfa, Turkiye
*Corresponding Author: Bahattin Cak, Department of Animal Science, Faculty of Veterinary, Van Yuzuncu Yil University, 6508, Van, Turkiye, Email: bahattincak@yyu.edu.tr
Online published on 11 October, 2024.
This study aims to determine the relationship between milk composition traits and breed in the Akkaraman and Awasi sheep as well as to provide ease of interpretation by showing the relationships structure between variables and between categories of variables in two-dimensional space with Categorical principal component analysis.
Categorical principal component analysis determines relationships between continuous and categorical variables as well as ordinal variables. It aims to reduce system dimensionality through optimal scaling while maintaining variable measurement levels (nominal, multiple nominal, ordinal and interval). In this research, data obtained from Akkaraman and Awasi Breed Sheep Raised by Public Hands in Tusba District of Van Province were used. In order to determine relationship with breed, the traits were divided into two categories, “low” and “high” and all variables (9 variables) were considered together and a Categorical principal components analysis was performed.
As a results, Dimension 1 accounted for 35.58% of the total variation while dimension 2 accounted for 15.21%. Two dimensions together accounted for 50.79% of the variation. Thus it can be noted that Categorical principal component analysis can be used in the analysis of data sets containing a large number of different types of variables with linear or non-linear relationships between them.
Animal husbandry, Configuration, Dimension reduction, Milk components
