Sentence examples for measures for models from inspiring English sources

In a review of articles, Collins et al. (2014) reported that the difference between observed and predicted external data was one of the key performance measures for models yet was often omitted from the publication and concluded that "It may, therefore, not be surprising that an overwhelming majority of developed prediction models are not used in practice".

The paper also contains several statistical measures for model selection.

This model utilises reliability measures, for modelling different aspects of the uncertainty.

Then, the design object dynamics must consider several measures for modelling these different sources.

In addition, GFI and NFI were also used even though they are not recommended as measures for model fit.

Additional features, for example target secondary structures have been shown to be a significant factor [25], [27], [30], [32] [34] in siRNA activity, and that feature set was not explored here, however adding target mRNA secondary structure features does not necessarily result in improved measures for model precision or accuracy if other features already dominate the model [32].

Some research groups included additional individual level socioeconomic measures for model 2 and in secondary analyses [see Supplemental Material, Table 1 (doi:10.1289/ehp.1002725)].

Hence, within the INLA framework, GLMMs can be fitted at low computational cost, giving access to various predictive measures for model comparison.

The Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), and the Root Mean Square Error of Approximation (RMSEA) were used as measures for model fit.

We used standardized cost-effectiveness measures for modeling T2D disease interventions, which allow comparison with other published findings and HRQoL of other diseases [ 38].

We report the Comparative Fit Index (CFI) and the Root Mean Square Error of Approximation (RMSEA) as measures for model fit.