【正文】 1 . 0 0 . 5 0 0 . 5 1 . 0 R E SU L T 1 , X e x p l : 5 8 % , 2 8 % C 1 _ H 1C 2 _ H 1C 3 _ H 1C 4 _ H 1C 1 _ H 3C 2 _ H 3C 3 _ H 3C 4 _ H 3C 1 _ H 2C 2 _ H 2C 3 _ H 2C 4 _ H 2R ED N E S SC O L O U RS H I N I N E SR . S M EL LR . F L A VS W EE T N ESS O U R N E SSB I T T ER N EO F F F L A VJ U I C I N EST H I C K N E SC H E W . R E SP C 1P C 2 B ip l o tPrincipal Component Analysis (PCA) Principles behind PCA The principles of Principal Component Analysis (PCA) X1 (Variable1) X3 (Variable 3) X2 (Variable 2) The original data points, plotted on the original axes of variables (X1, X2, X3) For convenience, we have assumed that the data points are in the shape of a cuboid. The original data points, plotted on the original axes of variables (X1, X2, X3) For convenience, we have assumed that the data points are in the shape of a cuboid. The principles of Principal Component Analysis (PCA) X1 (Variable1) X3 (Variable 3) X2 (Variable 2) The principles of Principal Component Analysis (PCA) X1 (Variable1) X3 (Variable 3) X2 (Variable 2) The original data points, plotted on the original axes of variables (X1, X2, X3) For convenience, we have assumed that the data points are in the shape of a cuboid. X1 X2 Samples PCA Scores Plot X1 (Variable 1) X3 (Variable 3) X2 (Variable 2) Instead, we use PCA to create “ new” variables, called Principal Components These are ordered as PC1, PC2, PC3, etc. PC1 explains the maximum variation of the data. PC2 then explains maximum of residual variation, etc. PC1 PC2 PC3 PCA Scores Plot Instead, we use PCA to create “ new” variables, called Principal Components These are ordered as PC1, PC2, PC3, etc. PC1 explains the maximum variation of the data. PC2 then explains maximum of residual variation, etc. PC1 PC2 PC3 PCA Scores Plot Instead, we use PCA to create “ new” variables, called Principal Components These are ordered as PC1, PC2, PC3, etc. PC1 explains the maximum variation of the data. PC2 then explains maximum of residual variation, etc. PC1 PC2 PC3 PCA Scores Plot PC1 PC2 Instead, we use PCA to create “ new” variables, called Principal Components These are ordered as PC1, PC2, PC3, etc. PC1 explains the maximum variation of the data. PC2 then explains maximum of residual variation, etc. PC1 PC2 PC3 Samples Scores Plot By default, after doing PCA on The Unscrambler, it is present in the top left corner PCA – Loadings Plot PC1 PC2 PC3 It is also necessary to find out how the individual PCs relate to the original Variables (X1, X2 and X3) To find that out, we refer to the Loadings Plot In the Loadings Plot, if two X variables are close together, they are highly correlated. PCA – Loadings Plot X1 (Variable 1) X3 (Variable 3) X2 (Variable 2) PC1 PC2 PC3 It is also necessary to find out how the individual PCs relate to the original Variables (X1, X2 and X3) To find that out, we refer to the Loadings Plot In the Loadings Plot, if two X variables are close together, they are highly correlated. PCA – Loadings Plot X1 (Variable 1) X3 (Variable 3) X2 (Variable 2) PC1 PC2 PC3 It is also necessary to find out how the individual PCs relate to the original Variables (X1, X2 and X3) To find that out, we refer to the Loadings Plot In the Loadings Plot, if two X variables are close together, they are highly correlated. PCA – Loadings Plot X1 (Variable 1) X3 (Variable 3) X2 (Variable 2) PC1 PC2 PC3 It is also necessary to find out how the individual PCs relate to the original Variables (X1, X2 and X3) To find that out, we refer to the Loadings Plot In the Loadings Plot, if two X variables are close together, they are highly correlated. PCA – Loadings Plot X1 (Variable 1) X3 (Variable 3) X2 (Variable 2) PC1 PC2 PC3 It is also necessary to find out how the individual PCs relate to the original Variables (X1, X2 and X3) To find that out, we refer to the Loadings Plot In the Loadings Plot, if two X variables are close together, they are highly correlated. PC1 PC2 Loadings Plot By default, after doing PCA on The Unscrambler, it is present in the top right corner PLEASE NOTE: PCs are orthogonal to each other, and therefore have no correlation with each other. X1 (Variable 1) X2 (Variable 2) X3 (Variable 3) PCA – Bi Plots PC1 PC2 Samples Scores Plot By default, after doing PCA on The Unscrambler, it is present in the top left corner PC1 PC2 Loadings Plot By default, a