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We apply this evaluation method to the Drosophila melanogaster PPI network [30] along with three network models, including linear preferential attachment model [19] and two biologically motivated network models [21], [22].
Several models including linear, linear quadratic and spline functions were applied.
We explored alternative regression models, including linear regression for all analyses, to ensure optimal fit of our selected models.
For models including linear terms of ozone, the pooled ozone coefficients were estimated by inverse variance methods (Harris et al. 2008).
GLMs were formulated by John Nelder and Robert Wedderburn as a way of unifying various other statistical models, including linear regression, logistic regression and Poisson regression [ 50].
Our method can be also applied to more general models such as generalized linear models, including linear regression and other non-linear regression models with some modification.
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Regression models include linear, quadratic, trigonometric, logarithmic and their rational forms for the same surface roughness problem.
The models include Linear Programming (LP), Weighted Goal Programming (WGP) and MINMAX Goal Programming (MINMAX GP), the GP variants of which are moreover formulated from a societal (S) and farmer (F) perspective.
These models included linear and nonlinear effects of age, sex, and race to control for these potential confounds.
In addition, models included linear terms for moving 7-day cumulative counts of influenza cases, and indicator variables for the day of the week.
A model including linear and quadratic effects of in vitro organic matter digestibility (ivOMd) provided a slightly inferior prediction of AMEn/GE values (R2 = 0.865).
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