<p>A, Comparison of PCA applied to the empirical data (left) and one selected simulation (right). The first (PC 1) and second (PC 2) principal components are represented here, where each point represents one of the analyzed populations, grouped by continents. B, Boxplots of the correlation values between the first two principal components in observations and simulations based on the prior distribution (“Prior”), 95% higher posterior density distribution (“95%HPD”), and on the point estimates (“Mode”).</p
<p>After varimax raw rotation, highly significant loading factors of the variables on the PCA axes a...
<p>The first four principal components (PCs) of a PCA for summary statistics calculated for 10,000 s...
<p>A, B and C: Hyperplane between the first and second principal components (PC1 and PC2, respective...
Each symbol represents an individual. (a) Shows the population structure of investigated major regio...
<p>Samples are projected onto the plane formed by the first two principal axes. The first factor exp...
Principal components analysis (PCA) is a multivariate ordination technique used to display patterns ...
Results for 56 pooled samples based on AAFpool of 22,324 SNPs are summarized. PCA scores of the firs...
<p>Principal Component Analysis (PCA) results on all individual samples at the level of OTUs cluster...
<p>Loading plots of the eigenvector coefficients of each feature analyzed by PCA show the influence ...
A to C show the results of the PCA based on the 2D plane corresponding to the first 2 axes. D to F s...
<div><p>The first principal component (PC) is plotted on the mean structure for various calculations...
<p>PCA was run on untransformed FA data (for correlations up to 0.1) and individuals have been super...
<p><b>A</b> Pareto plot of the percentage of the variance explained by each principal component (bla...
<p>For each PCA analysis, only three principal components are represented (PC1 <i>vs.</i> PC2, and P...
Principal component analysis (PCA) of the homologous polygon models, Pearson’s rs between perceived ...
<p>After varimax raw rotation, highly significant loading factors of the variables on the PCA axes a...
<p>The first four principal components (PCs) of a PCA for summary statistics calculated for 10,000 s...
<p>A, B and C: Hyperplane between the first and second principal components (PC1 and PC2, respective...
Each symbol represents an individual. (a) Shows the population structure of investigated major regio...
<p>Samples are projected onto the plane formed by the first two principal axes. The first factor exp...
Principal components analysis (PCA) is a multivariate ordination technique used to display patterns ...
Results for 56 pooled samples based on AAFpool of 22,324 SNPs are summarized. PCA scores of the firs...
<p>Principal Component Analysis (PCA) results on all individual samples at the level of OTUs cluster...
<p>Loading plots of the eigenvector coefficients of each feature analyzed by PCA show the influence ...
A to C show the results of the PCA based on the 2D plane corresponding to the first 2 axes. D to F s...
<div><p>The first principal component (PC) is plotted on the mean structure for various calculations...
<p>PCA was run on untransformed FA data (for correlations up to 0.1) and individuals have been super...
<p><b>A</b> Pareto plot of the percentage of the variance explained by each principal component (bla...
<p>For each PCA analysis, only three principal components are represented (PC1 <i>vs.</i> PC2, and P...
Principal component analysis (PCA) of the homologous polygon models, Pearson’s rs between perceived ...
<p>After varimax raw rotation, highly significant loading factors of the variables on the PCA axes a...
<p>The first four principal components (PCs) of a PCA for summary statistics calculated for 10,000 s...
<p>A, B and C: Hyperplane between the first and second principal components (PC1 and PC2, respective...