<p>Green triangles refer to soil samples of undisturbed sites whereas orange dots are samples of environmentally manipulated areas. The axes of the PCA ordination plot are a linear combination of environmental factors describing the soil samples and the vectors reflect the coefficients of these factors indicating the direction and strength of the correlation. The maximum possible strength of all correlations is indicated by the blue circle. Explained variance - PC1 axes: 20.2%; PC2 axes: 13.3%. The first 5 principal components accounted for 67% of the observed variance in the dataset.</p
<p>After varimax raw rotation, highly significant loading factors of the variables on the PCA axes a...
<p>Samples are projected onto the plane formed by the first two principal axes. The first factor exp...
<p>The first two principal components accounted for 77.4% of the variation. The four treatments (n =...
<p>Principal component analysis (PCA) of rhizosphere (triangle) and bulk (square) communities of sed...
<p>Boldface numbers are heavily weighted factors under each principal component (PC).</p><p>Results ...
<p>Principal Component Analysis (PCA) of 12 soil fertility measures for 134 plots.</p
<p>Factors 1 and 2 accounted for 45.10% and 33.90%, respectively, of the variance.</p
<p>Points indicate the principal component loadings of each variable included in the PCA analysis. S...
The objective of this research is to study principal component analysis for classification of 67 soi...
<p>Projection of the environmental variables (arrows) and the sampling dates (colored points) on the...
<p>The percentages show how much variation is explained by each principal component. The soils with ...
<p>The first two principal components (PC1 and PC2) accounted for 59.4% and 20.3% of total variance,...
Results for 56 pooled samples based on AAFpool of 22,324 SNPs are summarized. PCA scores of the firs...
<p>Dots represent sites within estuaries. Vectors show the two-dimensional (PC1 and PC2) correlation...
<p>Symbol shapes represent region for each of the samples. Vectors labelled as region (Reg), elevati...
<p>After varimax raw rotation, highly significant loading factors of the variables on the PCA axes a...
<p>Samples are projected onto the plane formed by the first two principal axes. The first factor exp...
<p>The first two principal components accounted for 77.4% of the variation. The four treatments (n =...
<p>Principal component analysis (PCA) of rhizosphere (triangle) and bulk (square) communities of sed...
<p>Boldface numbers are heavily weighted factors under each principal component (PC).</p><p>Results ...
<p>Principal Component Analysis (PCA) of 12 soil fertility measures for 134 plots.</p
<p>Factors 1 and 2 accounted for 45.10% and 33.90%, respectively, of the variance.</p
<p>Points indicate the principal component loadings of each variable included in the PCA analysis. S...
The objective of this research is to study principal component analysis for classification of 67 soi...
<p>Projection of the environmental variables (arrows) and the sampling dates (colored points) on the...
<p>The percentages show how much variation is explained by each principal component. The soils with ...
<p>The first two principal components (PC1 and PC2) accounted for 59.4% and 20.3% of total variance,...
Results for 56 pooled samples based on AAFpool of 22,324 SNPs are summarized. PCA scores of the firs...
<p>Dots represent sites within estuaries. Vectors show the two-dimensional (PC1 and PC2) correlation...
<p>Symbol shapes represent region for each of the samples. Vectors labelled as region (Reg), elevati...
<p>After varimax raw rotation, highly significant loading factors of the variables on the PCA axes a...
<p>Samples are projected onto the plane formed by the first two principal axes. The first factor exp...
<p>The first two principal components accounted for 77.4% of the variation. The four treatments (n =...