Sentence examples for variable models were from inspiring English sources
Competing latent variable models were identified in previous studies.
Regression results from individual and aggregate variable models were compared with the dispersion parameter-based R2 (R2α) and AIC.
Competing latent variable models were identified in previous studies and a priori model modifications were made to test derivations of the nine base models.
Moreover, the fact that all best forest-linked variable models were more robust for breeding abundance than for occurrence suggests that the focus of forest habitat control is linked to R. aurora abundance.
In this experiment, the best 3 5 variable models were selected according to the quality of the statistical parameters Q loo 2 and Q boot 2. Table 7 shows the best regression models and their corresponding statistical parameters, based on the QuBiLs-MAS 2D-indices.
Linear regression models were constructed with HRBS drug and sex sub-scores as the dependent variables and gender as the independent variable; models were adjusted for demographic variables followed by potential mediators selected a priori that were associated with both the independent and dependent variables at a level of statistical significance of p < 0.05 (Baron and Kenny 1986).
However, estimation of such lagged dependent variable models are, in general, inconsistent in the presence of fixed child or family effects.
Latent variable models are frequently characterized by poorly defined, multimodal likelihood functions, and this appears to be the case here.
Latent variable models are a group of methods that use the information from the manifest variables to identify subtypes of cases defined by the latent variable.
A range of multiple variable models was constructed using the estimation dataset with a combination of these variables depending upon their importance based on adjusted R2 values.
The tool is built using a probabilistic matrix factorization method and DrugBank v3, and the latent variable models are trained using the GraphLab collaborative filtering toolkit.
