Principal Component Analysis - Multidisciplinary Applications 2012
DOI: 10.5772/38835
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Application of the Principal Component Analysis to Disclose Factors Influencing on the Composition of Fungal Consortia Deteriorating Remained Fruit Stalks on Sour Cherry Trees

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Cited by 6 publications
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“…The relationships between host (wheat varieties and small grains) and Rhizoctonia strains (potential soil borne pathogens) have been analyzed by multivariate methods: Non-linear Mapping (NLM) [34], Cluster Analysis (CA), Principal Component Analysis (PCA), Regression Analysis combined with Canonical Correlation Analysis (CCA) and Potency Mapping (PM) technique [35] following a previously described scheme [36]. PCA was carried out on the correlation matrix [37] and only the components having an eigenvalue greater Susceptibility of Wheat Varieties to Soil-Borne Rhizoctonia Infection 2244 than one were included in the evaluation of data to demonstrate potential number of factors influencing on host parasite system, and the results were not delineated in details.…”
Section: Discussionmentioning
confidence: 99%
“…The relationships between host (wheat varieties and small grains) and Rhizoctonia strains (potential soil borne pathogens) have been analyzed by multivariate methods: Non-linear Mapping (NLM) [34], Cluster Analysis (CA), Principal Component Analysis (PCA), Regression Analysis combined with Canonical Correlation Analysis (CCA) and Potency Mapping (PM) technique [35] following a previously described scheme [36]. PCA was carried out on the correlation matrix [37] and only the components having an eigenvalue greater Susceptibility of Wheat Varieties to Soil-Borne Rhizoctonia Infection 2244 than one were included in the evaluation of data to demonstrate potential number of factors influencing on host parasite system, and the results were not delineated in details.…”
Section: Discussionmentioning
confidence: 99%