In each analysis, we noted for significance of model effect as well as independent variables that made a unique statistically significant contribution to the model.
In order to model effects such as eating a meal or physical activity, which can occur at random times, we will introduce stochastic terms into the model.
The final model, M2, investigated school effects as well as cross-level interactions.
The aggregate model captured price and income effects as well as demographic determinants of consumer behavior.
Because there are no explicit GABA interneurons in the simulation, we model their effects as an impediment to the excitability of the MSN by modifying the chloride conductance.
For all outcomes the fixed effect model as well as the random effects model was applied.
Genome scans were performed at parental level by modeling QTL effects as random terms in linear mixed models with identity-by-descent (IBD) kinship matrices.
The variance component approach modeled QTL effects as a random term, and estimates of the additive value of each individual at a given QTL were obtained with BLUPs.
Population models usually have fixed effect as well as random-effect parameters, and are therefore called "mixed-effect" models.
We calculated the pooled ORs with the fixed effects model as well as with the random effects model.
