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When a non-linear model includes interaction terms, the marginal effect of a variable is varied by other interacting variables, and estimated coefficients of interaction terms do not provide the change in the partial effect of either variable (Buis [33], Karaca-Mandic, Norton and Dowd [34], Jaccard [35] and Greene[36]).
The second model includes interaction variables for the boom period and for the LAC countries during the boom.
The model we employed for the analysis is as follows: Y i, j = D i + β g j + β i g j + P j + e i, j This model includes interaction terms (βi gj) for the change in the coefficient for the additive effect of genotype by day.
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Notice that the model includes interactions between pathogens, through the assumption that only the last infection of a given host is infectious.
The model includes interactions among the covariates.
Zuk et al. (2012) present an alternative proof of this equality for the general case where the genetic model includes interactions between variants and with environment.
The third and the most realistic model includes interactions among all the statistically significant demographic characteristics and the racial/ethnic variables.
Hence, if the selected model included interaction effects, the model was again fit with MCMCglmm to obtain Bayesian maximum posterior estimates and highest posterior density intervals with 95% support (HPDI95%) for parameter estimates of interaction effects [93].
Statistical second-order model including interaction and polynomial terms was generated for all the response variables.
In bold: selected models for which ΔDIC > 5, i.e. the model including interaction had a smaller DIC.
The Poisson's model including interaction between cohort and period was extrapolated for the years 2000 2010 to obtain future rates.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com