<p>A. Relative contributions of different acoustic parameters to PCA eigenvectors; the darkness of the print indicates the strength of contribution. B. Scree plot indicating the percent contribution by each eigenvector to the total variation along the axis. Plots of PC2 vs. PC1 for (C) the Alcohol treatment and (D) the Control treatment: unique colors denote individual birds; open and solid circles indicate daily values recorded in Phase II (no alcohol) and Phase III (alcohol) respectively; some individual values are not visible as they overlap.</p
Musicality can be thought of as a property of sound that emerges when specific organizational par...
Graphic of the generalized linear mixed model outputs using the principal component (PC) scores comp...
<p>(a) Two-dimensional plot of the first two factors extracted by principal components analysis over...
<p>Plotted are the values of least-square means of each acoustic feature (A–F) from whole-motif meas...
<p>Plotted are the individual and treatment group means of (A) number of lead notes per bout, (B) th...
<p>Depicted are the Eigenvectors of the first and second principal components of 15 acoustic variabl...
<p>Combined effects of the three sound parameters (pulse length, frequency, and intensity) shown as ...
<p>Principal component analysis (PCA) was used to reduce the dimension of the tertiary dataset compr...
<p>Shown are spectrograms of a representative syllable recorded in Phases II (no alcohol; left) and ...
<p>A bi-dimensional plotting of the principal component analysis using the five acoustic variables (...
Principal component analysis was used to construct quantitative phenotypes for alcoholism. These wer...
Principal components analysis (PCA) is a multivariate ordination technique used to display patterns ...
Two common artifacts that corrupt evoked responses are noise and background electroencephalogram (EE...
<p>Principal Component Analysis Bi-plot graph. Each dot represents an animal in the study and triang...
<p>Larger values on PC1 represent songs that are longer, that have more notes (that are shorter) and...
Musicality can be thought of as a property of sound that emerges when specific organizational par...
Graphic of the generalized linear mixed model outputs using the principal component (PC) scores comp...
<p>(a) Two-dimensional plot of the first two factors extracted by principal components analysis over...
<p>Plotted are the values of least-square means of each acoustic feature (A–F) from whole-motif meas...
<p>Plotted are the individual and treatment group means of (A) number of lead notes per bout, (B) th...
<p>Depicted are the Eigenvectors of the first and second principal components of 15 acoustic variabl...
<p>Combined effects of the three sound parameters (pulse length, frequency, and intensity) shown as ...
<p>Principal component analysis (PCA) was used to reduce the dimension of the tertiary dataset compr...
<p>Shown are spectrograms of a representative syllable recorded in Phases II (no alcohol; left) and ...
<p>A bi-dimensional plotting of the principal component analysis using the five acoustic variables (...
Principal component analysis was used to construct quantitative phenotypes for alcoholism. These wer...
Principal components analysis (PCA) is a multivariate ordination technique used to display patterns ...
Two common artifacts that corrupt evoked responses are noise and background electroencephalogram (EE...
<p>Principal Component Analysis Bi-plot graph. Each dot represents an animal in the study and triang...
<p>Larger values on PC1 represent songs that are longer, that have more notes (that are shorter) and...
Musicality can be thought of as a property of sound that emerges when specific organizational par...
Graphic of the generalized linear mixed model outputs using the principal component (PC) scores comp...
<p>(a) Two-dimensional plot of the first two factors extracted by principal components analysis over...