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In [ 42], an extension of logic regression was also proposed to identify SNP interactions explanatory for the disease status, with two measures devised for quantifying the importance of these interactions for the accuracy of disease prediction.
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For each colony and immune measure we fit generalised linear models with principal components that explained ≥ 5%% of the variation as response variables to homozygosity weighted by locus (HL) [ 52], sex and their interaction as explanatory terms, removing interactions if they were non-significant [ 71].
Therefore no interactions among explanatory factors were included in the model, and we did not attempt to model the temporal covariance among the repeated measures on each island.
Habitat effects on butterfly densities were investigated using GLMs assuming a Poisson distribution for the counts an using the two ground cover variables and their interactions as explanatory variables.
We modeled the proportion of parasitized caterpillars using GLM (pooling traps for a site) with the surface of open urban areas, the surface of artificial urban areas, the density of free flying butterflies and their interactions as explanatory variables and assuming a binomial error.
No interactions with explanatory variables were found at any locus.
We also tested interactions between explanatory variables and no interaction was significant.
Hypothesised interactions between explanatory variables were assessed in separate models by including an interaction term.
We tested all possible models including interactions among explanatory environmental and genetic variables.
None of the first-order interactions between explanatory variables of the final model were significant.
For reasons of practicality in both analysis and interpretation, only 2-way interactions between explanatory variables were examined.
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