Redundancy Analysis (RDA) and Principal Component Analysis (PCA) in Canoco5 скачать в хорошем качестве

Redundancy Analysis (RDA) and Principal Component Analysis (PCA) in Canoco5

Redundancy Analysis (RDA) and Principal Component Analysis (PCA) in Canoco5 Principal component analysis (PCA) is a popular technique for analyzing large datasets containing a high number of dimensions/features per observation, increasing the interpretability of data while preserving the maximum amount of information, and enabling the visualization of multidimensional data. Formally, PCA is a statistical technique for reducing the dimensionality of a dataset. Redundancy Analysis allows studying the relationship between two tables of variables Y and X. While the Canonical Correlation Analysis is a symmetric method, Redundancy Analysis is non-symmetric. In Canonical Correlation Analysis, the components extracted from both tables are such that their correlation is maximized. In Redundancy Analysis, the components extracted from X are such that they are as much as possible correlated with the variables of Y. Then, the components of Y are extracted so that they are as much as possible correlated with the components extracted from X. CONOCO5 CANOCO5 RDA PCA DCA Excel to Canoco5 Plotting in Canoco5 Editing Plots in Canoco5