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We removed variables from either category for which there were no data, or no variance (e.g., if all values were zero).
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A stepwise selection method was then performed to remove variables from the model.
If f 1 is less than the value of the F-random variable for removing variables from the model, such an F-random variable is referred to as f out.
R's stepAIC command was then invoked on the optimized multiple linear regression equation to systematically add and/or remove variables from the overall MLR equation with the optimality criterion of maximizing goodness of fit of the model predictions vs. observed values of DNA migrations and physico-chemical measurements.
We used p values of 0.1 to enter and remove variables from the model.
Stepwise backward elimination was used to remove variables from the model if their adjusted p-value > 0.05.
A backward procedure was then used to remove variables from the model (1% significance level), adjusting for familial environment characteristics.
The stepwise selection method was used to enter variables into and remove variables from the model (significance level of 0.15 for entering/removing variables).
Iterative PLS (IPLS) adds new variable(s) in the model or remove variables from the model if it improves the model performance [ 19].
Final models were selected by sequentially removing variables from the full model (backward selection), minimizing Akaike's Information Criterion (AIC) [ 20].
Variables with a P value < 0.1 in univariate analysis were included in the regression analysis, and then a P value of 0.2 was used to remove variables from the model.
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