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However, as mentioned above, it was not possible to model all predictors at all three levels.
Note that the global model (all predictors and first-order interactions) possessed the lowest AIC by 300 points, but with 240 model terms, was far less interpretable than the AIC next-best model with network size, network topology, recombination rate, average additive variance, and interactions as predictors.
In the second model, all predictors and all personal confounders were entered in the model.
In the clinical model, all predictors except body mass index were significantly associated with obstructive coronary artery disease.
In the final third model, all predictors and all personal and work-related confounders were entered in the model.
In a multivariate model, all predictors remained statistically significant predictors of measured NO2 annual concentrations (Table 3).
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Each of the modeling stages contains two versions of the model: a full model with all predictors, and a reduced model with only significant predictors.
Inferences were drawn from the full models (all predictors present) [ 45] and also after model simplification to confirm the stability of the models.
For each visual outcome, a summary of the initial model (including all predictors) and the final model (after backwards stepwise regression removed statistically redundant predictors) is shown.
McFadden R and deviance compare the final model (with all predictors) to a fully saturated model.
We considered a fully adjusted multivariate model containing all predictors as well as an univariate model for each predictor.
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sample all predictors
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