National Academy of Agricultural Sciences (NAAS)
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PRINT ISSN : 2319-7692
Online ISSN : 2319-7706 Issues : 12 per year Publisher : Excellent Publishers Email : editorijcmas@gmail.com / submit@ijcmas.com Editor-in-chief: Dr.M.Prakash Index Copernicus ICV 2018: 95.39 NAAS RATING 2020: 5.38 |
The principal component analysis (PCA) is used to identify the most influencing variable. It is one of the statistical techniques for reducing the dimension of the data. The study was conducted in Coimbatore district, Tamil Nadu with 340 profile points. More than 30 environmental covariates are available for this analysis. To make the analysis easier and accurate the data has to be reduced. The principal components (PC1, PC2, PC3 and PC4) are selected for further analysis which accounts for 53.84% of variation. From the selected four principal components the variables which are having higher percentage of variation were identified. Hence it is one of the easiest methods to predict the most influencing variable using R software.
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