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To address this problem we repeated the analysis with a simulated one-directional electrical coupling (gcoupl = 100 nS, from the fixed model to the model with changed parameter), similar to the procedure of the model fitting.
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Then analyzing different properties of this model with change of parameters; discussing the relation between v fs and these parameters.
This creates a "fixed-form" model, with changes in the functioning coming through extensive code re-writes and careful redesign around logical structures.
Later, Krolzig (1997) carries out a characterization of the different variables of the Markov-switching model, with changes in the mean, variance and/or intercept.
A partial equilibrium approach is used to establish the link between price changes estimated by price model with changes in final demand derived from the classical demand driven model.
We used a linear mixed effects model with change in provisioning rate as the response variable (number of feeds during playback minus number of feeds in control period), with nestling stage (mid/late), partner sex and control feeding rate as explanatory variables.
We first evaluated each health outcome in a bivariate model with change in RSDI score as the independent variable.
When change in fitness was evaluated in the regression model with change in A1C (model K), we found that each predicted hs-CRP change.
Intermediate indicators (independent variables) with significant changes in means entered the regression model with changes in outcomes as subsequent dependent variables.
Change in percentage energy from each macronutrient was calculated and entered into the model with change in total energy included as a covariate.
A linear regression model with change in behaviour from 2009 to 2011 as the outcome was used in the final model allowing adjustments for confounding variables.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com