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The addition of interaction terms did not improve the model and tended to offset the main effects, therefore it is not proposed to recommend the models with interactions.
We adjusted for tobacco smoke exposure and asthma medication use, which were not significant predictors nor did they influence our main effects, therefore we eliminated these variables in our final model.
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The interaction model is not free of limitations; in particular, it is important to note that the genetic covariance between any pair of environments is represented by the variance of the main effect; therefore, it is restricted to being positive and the same for all pairs of environments analyzed jointly.
This happens because, in the interaction model, the covariance is represented by the variance of the main effects and, therefore, it is bound to be non-negative.
However, two-factor or higher-order interaction effects can confound the main effects, and therefore, individual RPLC factors affecting chromatographic deuterium isotope effect may be underestimated by the approach.
Although we observed for FT highly significant epistatic interactions, these explained only a low proportion of the genetic variance compared to the main effects and, therefore, might be disregarded in marker-assisted selection for this trait.
The main effect seems therefore to be related to d increase.
The confounders with significant difference or contributing to the change of main effect were therefore recruited as covariate factors in the model.
In a design with K attributes where n k represents the number of levels of attribute k, the total number of possible outcomes (pairs) in a main effects design is therefore 2 ∑ i = 1 K − 1 [ n i ∑ k = i + 1 K n k ], or 72 unique pairs in the case of this study.
The main effects of our results therefore referred to this posterior reading triangle.
Therefore, main effects are interpreted cautiously in case of significant interactions.
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