<p>(A) Variable loadings for first and second principal components, 17 DAS and (B) Analysis by ecotype over first and second principal components, 17 DAS. Significance level is given by the size of the squares, the smaller the square the more significantly different (P<0.05). (C) Contrast in ‘Area’ between ‘Bur-0’ and ‘Wu-0’ and Contrast in ‘Compactness’ between ‘Ct-1’ and ‘No-0’) and where both ecotypes have similar Area as demonstrated in plot (A).</p
<p>Principal Component Analysis of Fourier coefficients: scatter plots of PC1 versus PC2 and PC2 ver...
Principal Component Analysis (PCA) is viewed as a descriptive multivariate method for a set of n obs...
<p>The first two principal components accounted for 77.4% of the variation. The four treatments (n =...
<p>A, B and C: Hyperplane between the first and second principal components (PC1 and PC2, respective...
<p>Loadings plot of PC1 versus PC2 for the two main principal component analysis obtained in the PCA...
<p>The first principal component accounts for 94.7% of variation and the second component explained ...
<p>Principal components analysis of size-regressed measurements, with loadings of each measurement f...
<p>(a) Variation explained by Principal Components. We retain the first four Principal Components wh...
<p>The percentage of explained variation for of each principal component is given in parentheses. Ch...
A to C show the results of the PCA based on the 2D plane corresponding to the first 2 axes. D to F s...
<p><b>A</b> Pareto plot of the percentage of the variance explained by each principal component (bla...
<p>(A) Histogram of eigenvalues, (B) Principal Component Analysis (PCA) plot of factor loadings in t...
<p>Loading plots of the eigenvector coefficients of each feature analyzed by PCA show the influence ...
<p>(a) Factor loadings of 18 PAHs on two components, and (b) factor scores of sampling locations on ...
<p>Principal component analysis of protein data: (A) scores plot of the samples (triangle filled wit...
<p>Principal Component Analysis of Fourier coefficients: scatter plots of PC1 versus PC2 and PC2 ver...
Principal Component Analysis (PCA) is viewed as a descriptive multivariate method for a set of n obs...
<p>The first two principal components accounted for 77.4% of the variation. The four treatments (n =...
<p>A, B and C: Hyperplane between the first and second principal components (PC1 and PC2, respective...
<p>Loadings plot of PC1 versus PC2 for the two main principal component analysis obtained in the PCA...
<p>The first principal component accounts for 94.7% of variation and the second component explained ...
<p>Principal components analysis of size-regressed measurements, with loadings of each measurement f...
<p>(a) Variation explained by Principal Components. We retain the first four Principal Components wh...
<p>The percentage of explained variation for of each principal component is given in parentheses. Ch...
A to C show the results of the PCA based on the 2D plane corresponding to the first 2 axes. D to F s...
<p><b>A</b> Pareto plot of the percentage of the variance explained by each principal component (bla...
<p>(A) Histogram of eigenvalues, (B) Principal Component Analysis (PCA) plot of factor loadings in t...
<p>Loading plots of the eigenvector coefficients of each feature analyzed by PCA show the influence ...
<p>(a) Factor loadings of 18 PAHs on two components, and (b) factor scores of sampling locations on ...
<p>Principal component analysis of protein data: (A) scores plot of the samples (triangle filled wit...
<p>Principal Component Analysis of Fourier coefficients: scatter plots of PC1 versus PC2 and PC2 ver...
Principal Component Analysis (PCA) is viewed as a descriptive multivariate method for a set of n obs...
<p>The first two principal components accounted for 77.4% of the variation. The four treatments (n =...