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Coding decisions were based on variable distributions and comparison of Akaike information criterion statistics.
Information criterion statistics suggest the four-class model best fits the data.
Adjusted odds ratios (AORs) significant at 95% CIs and Schwarz Criterion statistics are reported for goodness-of-fit.
Performance of the models to inform the inclusion of variables was assessed using C-statistics, Akaike, and Bayesian information criterion statistics.
A pure linear model fitted the data nominally better than did a pure dose-squared model (difference in Akaike information criterion statistics of 2.47).
However, information criterion statistics such as the Akaike Information Criterion AICC), available in Latent Gold Version 3.0, have been developed to provide an idea of the relative distance of two or more models from a theoretical best model [ 37].
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Lower Akaike Information Criteria statistics were considered to indicate a better statistical fit of the model to the data.
Table 3 Forecasting error criteria statistics of different SSCBs SSCBs Ramp start time Ramp duration Ramp end time Ramp swing Ramp rate NMAE RMSE (min) NMAE RMSE (min) NMAE RMSE (min) MAE RMSE (p.u).
Akaike Information Criteria statistics were used to compare the fit of models based on questionnaire and accelerometer assessments.
To select among error autocorrelation structures, nested models were compared using likelihood-ratio tests and non-nested models were compared using information criteria statistics (Pinheiro and Bates, 2000).
We compared Akaike information criterion (AIC) statistics for fixed-slope and random-slope models and chose models with lower AIC statistics for presentation.
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