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Moreover, our analysis revealed that the phase shift between the two predictors was significant (Supplementary Fig. S4).
Activity across the entire striatal ROI was significantly associated with unique MS-PE (t(18)=4.172, P<0.001, Cohen's d=0.957) but not with unique ERO-PE (t(18)=1.162, P=0.260, Cohen's d=0.266), although the difference between the two predictors did not reach significance (t(18)=1.558, P=0.137, Cohen's d=0.357).
The overall sensitivity and specificity over the predictions is nearly the same between the two predictors.
During the testing process, only the sequences of proteins in Online Supporting Information S1 but not their accession numbers were used as inputs in order to make the comparison between the two predictors under exactly the same condition.
To the extent that nutrients and SST are correlated, the statistical model is unable to distinguish the effects of one predictor from the other; the chlorophyll response is divided between the two predictors.
The interaction value can also be thought of as the relative contribution of the interaction between the two predictors towards the overall predictive performance of the individual model (the cv deviance value).
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At the same time, the hypothesized interaction between the two predictor variables was not supported.
All forms of two-factor interactions, as well as three-factor interactions between the three predictors were included in the model.
Figure 1 illustrates the interplay between the three predictors.
We then go on to describe the differences between the three predictors.
Within each cross-validation step, variable selection between the three predictors was performed using best subset selection with the AIC criterion (Akaike's An Information Criterion).
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between the two parameters
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between the two regressors
between the two pointers
between the two explanatory
between the two people
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