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The present analyses show that the R values were modest for the regression models; therefore, other potential explanatory factors, especially direct tissue markers [ 45], must be identified to construct an optimal explanatory model (Table 3).
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However, using usual stepwise regression to find the optimal combination of explanatory variables to model a response is often considered to be a high-variance operation because small perturbations of the response data can sometimes lead to vastly different subsets of the variables [51], [52].
This is achieved by finding optimal splits of some suitable explanatory variables at a given threshold value, such that a defined impurity function is minimised.
To prevent overparameterization of the model, we use only trend variables selected through a geostatistical adaptation of the Bayesian information criterion (BIC), wherein models with different subsets of trend variables are compared based on their BIC score, and the model with the lowest BIC score is optimal in terms of its explanatory power relative to its complexity.
The depended outcome variable was the level of blood pressure (optimal, pre- and hypertension) and explanatory variables included place of residence and indicators of parental SES: family size, parental educational attainments and occupation status, income adequacy and family wealth.
All markers on the same chromosome of a major-effect locus were considered for inclusion as explanatory variables in the optimal model.
The debates over 'adaptationism' turned out to involve a diffuse set of worries about whether evolution produces optimal designs, the methodological role of optimality assumptions, and the explanatory goals of evolutionary theory.
Model parameters of the optimal wood density model, which included all explanatory variables proposed by Franceschini et al. (2010) and additional interactions with taxon, are given in Table 3.
Standard linear regression analysis then was conducted using the numerical explanatory variables obtained with the optimal scaling method.
Other more important variables, especially those for characterizing natural selection (e.g., the frequency of usage of optimal codons [ 45]), might increase the explanatory power of natural selection on codon usage patterns of virus genomes.
There was no significant association between any of the explanatory variables (including ANC) with either optimal thermal care or good neonatal feeding.
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