A. 2D scatter plot showing each fly as a dot, and the mean and standard error of the factor loadings as bars. Abscissa is the first principal component (PC1). Ordinate is PC2 on the upper panel and PC3 on the lower panel. B. Snapshot of the corresponding 3D representation plot. Each dot corresponds to a fly coordinate, bars are means and standard errors, and the ellipsoids represent the 80% confidence interval (calculated from the covariance using the loadings on the three first principal components)
<p>(A) Principal component analysis (PCA). The 32 samples are projected onto the 2D plane such that ...
<p>A, Comparison of PCA applied to the empirical data (left) and one selected simulation (right). Th...
<p><b><i>A</i></b>, first and second principal components. <b><i>B</i></b>, first and third principa...
A. 2D scatter plot showing each fly as a dot, and the mean and standard error of the factor loadings...
<p>Three PCs were retained that explained 56% total variance, with PC1, PC2, and PC3 explaining 22%,...
(a) Original data without normalization. (b) Data normalized by integrated normalization. Both plots...
<p>Cumulative explained variance and the number of principal components are shown for each movement ...
<p>Plots of the principal coordinate analysis (PCA) from the covariance matrix with data standardiza...
<p>A, B and C: Hyperplane between the first and second principal components (PC1 and PC2, respective...
<p>Flight mice data points clustered relatively tightly within the bottom right quadrant, whereas gr...
*<p>Significance (P = 0.05, 0.01, 0.001).</p>a<p>relative to young (day 3) flies.</p
<p>Principal Component Analysis Bi-plot graph. Each dot represents an animal in the study and triang...
<p>Eigen values and coefficients (loadings) of the first two principal components (PC1 and PC2) for ...
grantor: University of TorontoHave covariances among characters been relatively static th...
<p>The vectors in the map indicate the relative contribution of each viewing-time variable to the pr...
<p>(A) Principal component analysis (PCA). The 32 samples are projected onto the 2D plane such that ...
<p>A, Comparison of PCA applied to the empirical data (left) and one selected simulation (right). Th...
<p><b><i>A</i></b>, first and second principal components. <b><i>B</i></b>, first and third principa...
A. 2D scatter plot showing each fly as a dot, and the mean and standard error of the factor loadings...
<p>Three PCs were retained that explained 56% total variance, with PC1, PC2, and PC3 explaining 22%,...
(a) Original data without normalization. (b) Data normalized by integrated normalization. Both plots...
<p>Cumulative explained variance and the number of principal components are shown for each movement ...
<p>Plots of the principal coordinate analysis (PCA) from the covariance matrix with data standardiza...
<p>A, B and C: Hyperplane between the first and second principal components (PC1 and PC2, respective...
<p>Flight mice data points clustered relatively tightly within the bottom right quadrant, whereas gr...
*<p>Significance (P = 0.05, 0.01, 0.001).</p>a<p>relative to young (day 3) flies.</p
<p>Principal Component Analysis Bi-plot graph. Each dot represents an animal in the study and triang...
<p>Eigen values and coefficients (loadings) of the first two principal components (PC1 and PC2) for ...
grantor: University of TorontoHave covariances among characters been relatively static th...
<p>The vectors in the map indicate the relative contribution of each viewing-time variable to the pr...
<p>(A) Principal component analysis (PCA). The 32 samples are projected onto the 2D plane such that ...
<p>A, Comparison of PCA applied to the empirical data (left) and one selected simulation (right). Th...
<p><b><i>A</i></b>, first and second principal components. <b><i>B</i></b>, first and third principa...