a<p>Principal component axes 1–3 of 7 are shown. The dominant microhabitat was recorded at each trapping station as one of 48 microhabitat categories. The categories shown here are simplifications of the field categories derived by a model simplification procedure. Trap station microhabitats were treated as pseudo-random samples of the microhabitats in each study site. Bold values are component loadings >+/−0.40.</p
<p>Principal component (PC) loadings of covariates relevant for modeling tiger occupancy, Eigen valu...
<p>(<b>A</b>) Eigenvalues as measures of the total variability explained by each principal component...
*<p>The data of pupal mines were also included (see text for the reason).</p>†<p>Variation explained...
<p>Eigenvalues were 2.7 and 2.0 for PC1 and PC2 respectively, explaining 30 and 22% of the variance ...
<p>Vectors show the strength and direction of the relationship between the microhabitat variables an...
<p>Principal components analysis of size-regressed measurements, with loadings of each measurement f...
<p>Principal component analysis (α = 0.05) showing linear patterns of a wide range of variables acro...
<p>Variables with correlation coefficients higher than 0.50 are highlighted in bold.</p
In this study, we used bootstrap simulation of a real data set to investigate the impact of sample s...
<p>After varimax raw rotation, highly significant loading factors of the variables on the PCA axes a...
<p>Entries are component loadings; eigenvalues and percentage of variation explained for the three s...
Statistics from the principal component analysis and the corresponding phylogenetic ANOVAs of PC1-PC...
Loadings for the relevant principal component (eigenvalue >1) from a principal component analysis of...
<p>(A) Variable loadings for first and second principal components, 17 DAS and (B) Analysis by ecoty...
<p>Set 1 (a) and 2 (b) data obtained from mild/moderate and severe autism groups were analyzed with ...
<p>Principal component (PC) loadings of covariates relevant for modeling tiger occupancy, Eigen valu...
<p>(<b>A</b>) Eigenvalues as measures of the total variability explained by each principal component...
*<p>The data of pupal mines were also included (see text for the reason).</p>†<p>Variation explained...
<p>Eigenvalues were 2.7 and 2.0 for PC1 and PC2 respectively, explaining 30 and 22% of the variance ...
<p>Vectors show the strength and direction of the relationship between the microhabitat variables an...
<p>Principal components analysis of size-regressed measurements, with loadings of each measurement f...
<p>Principal component analysis (α = 0.05) showing linear patterns of a wide range of variables acro...
<p>Variables with correlation coefficients higher than 0.50 are highlighted in bold.</p
In this study, we used bootstrap simulation of a real data set to investigate the impact of sample s...
<p>After varimax raw rotation, highly significant loading factors of the variables on the PCA axes a...
<p>Entries are component loadings; eigenvalues and percentage of variation explained for the three s...
Statistics from the principal component analysis and the corresponding phylogenetic ANOVAs of PC1-PC...
Loadings for the relevant principal component (eigenvalue >1) from a principal component analysis of...
<p>(A) Variable loadings for first and second principal components, 17 DAS and (B) Analysis by ecoty...
<p>Set 1 (a) and 2 (b) data obtained from mild/moderate and severe autism groups were analyzed with ...
<p>Principal component (PC) loadings of covariates relevant for modeling tiger occupancy, Eigen valu...
<p>(<b>A</b>) Eigenvalues as measures of the total variability explained by each principal component...
*<p>The data of pupal mines were also included (see text for the reason).</p>†<p>Variation explained...