<p>This ranking represents the consensus among all 12 datasets as given by the first principal component () of a Principal Component Analysis run on all polymorphic ranks, explaining of the variance and representing the agreement. See <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045198#pone.0045198.s001" target="_blank">Materials S1</a></b> for details and WALS <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045198#pone.0045198-Haspelmath2" target="_blank">[31]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045198#pone.0045198-Dryer1" target="_blank">[32]</a> for the description of the features.</p
<p>Curves show the cumulative sum of variance explained by increasing numbers of principal component...
*<p>0.1 min was added to the retention time of each compound.</p><p>The importance of each feature w...
<p>(a) The Principal component analysis (PCA) energy ranking, in which the top five Principal compon...
<p>The <b>Rank</b> represents the feature’s rank from the most “stable” (top) to the most “unstable”...
<p>Loading plots of the eigenvector coefficients of each feature analyzed by PCA show the influence ...
<p>(A) The percent variability explained by each principal component (<a href="http://www.plosone.or...
<p>SVM-REF ranked the features according to their ability to separate different categories for each ...
<p>The first principal component accounts for 94.7% of variation and the second component explained ...
<p>Multivariate data (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104970#...
Principal component 1 (PC1) in horizontal axis and PC2 in vertical axis explain 37% and 15% of varia...
<p>The features (abbreviated names are transparently based on the full WALS names and as for <a href...
<p>The ‘mini-core’ set is shown in red and it is composed of the first 10% top-ranked accessions by ...
<p>Stability is measured as the percent of voxels in common among the subsets of <i>k</i> top variab...
<p>The stabilities (as relative ranks from 0.0 = most unstable to 1.0 = most stable) of the shared f...
<div><p>The first principal component (PC) is plotted on the mean structure for various calculations...
<p>Curves show the cumulative sum of variance explained by increasing numbers of principal component...
*<p>0.1 min was added to the retention time of each compound.</p><p>The importance of each feature w...
<p>(a) The Principal component analysis (PCA) energy ranking, in which the top five Principal compon...
<p>The <b>Rank</b> represents the feature’s rank from the most “stable” (top) to the most “unstable”...
<p>Loading plots of the eigenvector coefficients of each feature analyzed by PCA show the influence ...
<p>(A) The percent variability explained by each principal component (<a href="http://www.plosone.or...
<p>SVM-REF ranked the features according to their ability to separate different categories for each ...
<p>The first principal component accounts for 94.7% of variation and the second component explained ...
<p>Multivariate data (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104970#...
Principal component 1 (PC1) in horizontal axis and PC2 in vertical axis explain 37% and 15% of varia...
<p>The features (abbreviated names are transparently based on the full WALS names and as for <a href...
<p>The ‘mini-core’ set is shown in red and it is composed of the first 10% top-ranked accessions by ...
<p>Stability is measured as the percent of voxels in common among the subsets of <i>k</i> top variab...
<p>The stabilities (as relative ranks from 0.0 = most unstable to 1.0 = most stable) of the shared f...
<div><p>The first principal component (PC) is plotted on the mean structure for various calculations...
<p>Curves show the cumulative sum of variance explained by increasing numbers of principal component...
*<p>0.1 min was added to the retention time of each compound.</p><p>The importance of each feature w...
<p>(a) The Principal component analysis (PCA) energy ranking, in which the top five Principal compon...