<p>We used PCA to describe dewlap and ventral patch hue, chroma, and brightness as an independent color score (based on the first PCA axis, PC1) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101515#pone.0101515-Endler1" target="_blank">[36]</a>. Factor loadings indicate the relative direction and magnitude of contribution by each color component to a score.</p
<p>Principal components diagram of treatments (a) show the overall light reaction norm of morphologi...
Principal components analysis (PCA) is a multivariate ordination technique used to display patterns ...
<p>Distributions in relation to the principal component factor 1 and 2 (A), factor 1 and 3 (B), and ...
Color data as interpreted in a color opponency framework. Raw color data are provided along with nor...
<p>A. Correlation circle of the variables used for the PCA, projected on the first two components. B...
<p>Note: Each colored point represents a sample. The first, second and third principal components ar...
<p>A, B and C: Hyperplane between the first and second principal components (PC1 and PC2, respective...
<p>A: whole experiment dataset; B: Tendril dataset; C: Inflorescence dataset. Percent of variation e...
A to C show the results of the PCA based on the 2D plane corresponding to the first 2 axes. D to F s...
The figure shows the first two principal components after merging the acquired datasets. The samples...
<p>Factor loading plot of proportion of positive probes further illustrates the PCA plot from <a hre...
<p>(A) Histogram of eigenvalues, (B) Principal Component Analysis (PCA) plot of factor loadings in t...
<p>The figure shows a biplot a PCA performed on the EsSense and PrEmo emotions. Every data point rep...
<p>The first two principal components accounted for 77.4% of the variation. The four treatments (n =...
<p>Each PCA vector represents a specific combination of the 1656 chemical descriptors. The three mos...
<p>Principal components diagram of treatments (a) show the overall light reaction norm of morphologi...
Principal components analysis (PCA) is a multivariate ordination technique used to display patterns ...
<p>Distributions in relation to the principal component factor 1 and 2 (A), factor 1 and 3 (B), and ...
Color data as interpreted in a color opponency framework. Raw color data are provided along with nor...
<p>A. Correlation circle of the variables used for the PCA, projected on the first two components. B...
<p>Note: Each colored point represents a sample. The first, second and third principal components ar...
<p>A, B and C: Hyperplane between the first and second principal components (PC1 and PC2, respective...
<p>A: whole experiment dataset; B: Tendril dataset; C: Inflorescence dataset. Percent of variation e...
A to C show the results of the PCA based on the 2D plane corresponding to the first 2 axes. D to F s...
The figure shows the first two principal components after merging the acquired datasets. The samples...
<p>Factor loading plot of proportion of positive probes further illustrates the PCA plot from <a hre...
<p>(A) Histogram of eigenvalues, (B) Principal Component Analysis (PCA) plot of factor loadings in t...
<p>The figure shows a biplot a PCA performed on the EsSense and PrEmo emotions. Every data point rep...
<p>The first two principal components accounted for 77.4% of the variation. The four treatments (n =...
<p>Each PCA vector represents a specific combination of the 1656 chemical descriptors. The three mos...
<p>Principal components diagram of treatments (a) show the overall light reaction norm of morphologi...
Principal components analysis (PCA) is a multivariate ordination technique used to display patterns ...
<p>Distributions in relation to the principal component factor 1 and 2 (A), factor 1 and 3 (B), and ...