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The first principal component explained a proportion of 94.6% of the total signal power.
On unforced principal components factor analysis, one component explained a high percentage (79%) of the variability in the AIA data.
For example, the annual component explained a portion of variability for all metrics and indices, ranging from a low of 5% of total variance explained (for Diptera richness) to a high of 35% (for % intolerant).
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The first component of the PLS-DA model explained approximately 64% of the variation, and the second component explained an additional 35% of the variation.
Remarkably, this "bad luck" component explains a far greater number of cancers than do hereditary and environmental factors.
These seven components explained a total of 67.2% of the variance.
These six components explained a total of 67% of the variance.
The participant and flashcard components explained a small proportion of variance (5.1% and 15.3%, respectively).
Together these three components explained a total of 33.6% of the variance.
The rest of principal components explained a lower amount of experimental variance: PC2 – 19.5%, PC3 – 10.5% and PC4 – 7.7%.
The participant and session components explained a small proportion of variance (15.1% and 8.2%, respectively) reflecting small systematic differences among participants and sessions.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com