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Landscape variables were first analyzed using principal component analysis (PCA), and then general linear models containing all combinations of principal components 1 3 (PC1–3) and landscape diversity measured using Simpson's Diversity Index (D) were compared using an adjusted Akaike's Information Criterion (AICc).
Among other things, this means that AIC model statistics are not defined for "full" models containing all possible variables.
In none of the full models containing all possible interaction terms, it was possible to identify significant moderation by age × gender (see Table 6).
Setting aside the models containing all "significant" variables allows us to focus more attention to the implied mechanisms at work in more parsimonious models.
We compared models containing all possible combinations of one or more explanatory variables and their interactions and used AIC values to select the most likely models.
Cumulative R2 calculated from models containing all preceding variables Higher Severity of Asthma scores indicate greater severity.
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a Models contain all demographic and socioeconomic indicators.
The first set of models contained all maternal components together (they are independent so there is no confounding) and were also adjusted for gender.
The primary PLS models contained all individual x and y variables, while the secondary models contained all or defined blocks or sets of influential (VIP > 1.0) x variables and excluded the occlusal caries y variables in the Y matrices due to individual Q-values below 0.1 (10%).
Regression analysis was carried out to reach the model containing all the nominated subtests.
Briefly, proteins were represented by the united-atom model containing all heavy atoms and polar hydrogens with an implicit solvent model.
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