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Sex-specific models and models with interactions between the JPI and ESS rounds (models (2) and (4)) on the total sample allow us to investigate this issue further.
Taking into account the observed results and the analysis of variance, all the responses were successfully adjusted to second order models with interactions, showing good R2 values and correlating the experimental data properly.
Regression models revealed significant interactions between geo-climatic descriptors and land use for a third of the models, with interactions accounting for up to 17% of the model's deviance.
Because species-area relationships may differ by feedstock, we also included models with interactions between patch size and feedstock type.
For this purpose we used exhaustive testing of all 26 = 64 linear regression models with interactions among predictors excluded for each of the 2×6 outcomes of interest.
Also, models with interactions were calculated.
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Results of logistic regression models with interaction terms suggest that none of the variables interacted with condition.
Next we investigated if models with interaction terms (models 10 and 15) would predict more accurately than the additive model.
Based on the multivariable models with interaction terms a decision model for treatment choice was developed.
*p values between regression slopes were obtained from linear regression models with interaction terms 13613_2018_391_MOESM2_ESM.tiff Additional file 2: Figure S2.
In this section, we present the results of the estimated flexible hierarchical logit models with interaction effects.
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