The present model is compared with the model examined in the previous paper.
Therefore, in these cases, the bifurcation structure of this very complex multi-compartmental model, which is not always possible to determine directly, is qualitatively identical to the reduced, one-compartment model examined in the previous sections.
The six-factor 19-item model examined in CFA-4 produced good model fit (χ=641.63, df=137, p=0.000, CFI=0.981, RMSEA=0.035), and CFA-5 results largely support model invariance across these five settings (ΔCFI=0.001, Δχ p=0.01).
To facilitate comparison of this extended predictor model (including financial difficulty and adverse life events) with the model examined in the primary analysis, a model without the additional predictor variables was examined and is provided in Additional file 1: Table S3.
Their results indicate that the univariate NN model significantly outperformed four alternative univariate models examined in prior research.
In predicting amphibian distributions, models examined in this study that relied upon expert-derived resistance values performed worse than null models with uninformative resistance values.
Multilevel regression models examined in 2015 the independent and joint associations of the three environmental measures with change in waist circumference, accounting for socio-demographic covariates.
The predictive accuracy of the fatigue models examined in this article was compared by applying them to four material systems for which extensive fatigue data was available.
Most of the models examined in this chapter have been applied to the rendering of a variety of materials, from marble to milk.
From this study, it appears that the proposed approaches provide enhanced analysis accuracy in estimating the torsional capacities of SFRC members, and closely capture their overall torsional responses compared to other models examined in this study.
