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The model in both cases consists of mean, factor effects and interaction effects.
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This is similar to using grand mean in a "factor effects" ANOVA model (compared to "cell means" ANOVA model).
The greater the F value was from unity, the more certain it was that the factors explain adequately the variation in the data about their mean, and the estimated factor effects were real.
Analysis of the global effect of each mean factor (diet or genotype) was complemented by a two class unpaired comparison of Significance Analysis of Microarray (SAM) [ 54] using the Microsoft Excel software.
The treatment combinations are chosen to provide sufficient information to determine the factor effects using the analysis of means.
The cell means parameterization is equivalent to the more common factor effects parameterization [57] but is more convenient for simulating specific epistatic scenarios.
We then studied the factor effects on the spatial distribution of detections by means of the extent of propagation in the area.
Estimate factor effects.
This means that the larger the factor effect for a parameter, the larger the effect the variable has on the process, or, in other words, the more significant the effect of the factor.
Mean effect of factors and their interactions were assessed by two‐way ANOVA.
Table 4 shows the sum of squares being used to estimate the factors' effect and the F ratios, which are defined as the ratio of the respective mean-square-effect to the mean-square-error (Bingol et al. 2010).
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