Sentence examples for model fit reporting from inspiring English sources

Hooper et al [ 54] have suggested that, while there are no golden rules for assessment of model fit, reporting a variety of indices is necessary because different indices reflect different aspects of a model fit.

The parameters and model fits reported here correspond to the inhibition model of Equation (24) with binding to state 2. Note that analysis of the data sets from isoforms from rat kidney, rat liver, and bovine heart does not provide a complete set of parameters for any of these isoforms.

In the model fits reported here the basic, non-return response threshold was fixed to 1.

Additionally the traditional chi-square test statistics for the baseline model and model fit are reported.

For model fit, we report Log Bayes Factors (LBF) – the difference in AIC between each model and a reference model.

Model fit was reported by the c-statistic, a measure which ranges from 0.5 for model fit no better than by chance to 1.0 for perfect fit.

The parameters of the consensus model and the standard errors of these parameters based on the logistic regression model fit are reported in Appendix Fig S2.

First, the global model fit statistic, reported as a chi-square, is used to assess the overall fit with a statistically significant (p <0.05) result indicating an ill-fitting model [ 47].

The model was calibrated using HCV prevalence by age and gender in the year 2000, as reported by NHANES. 1 The incidence was back-calculated and the model was used to fit reported prevalence in 2000.

The resulting model fits the reported design conditions of two separate pilot-scale studies (ranging from 0.4 to 10 MW heat transfer duty).

With regard to selecting model fit statistics to report, Kline (2010) recommends reporting the Chi-squared test, the RMSEA, the CFI, and the SRMR.