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When heart rate was included in the model, model results were similar, although with slightly lower coefficients.
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Adding illness duration and several demographic characteristics to the model (model 1) results in a larger random effect of the age of diagnosis for men (not for women), whereas the random effects of the year of diagnosis almost completely disappear.
Within each of the 100 permutations, the statistical model (Model 6, see Results) was fit to the permuted data.
As a result, another model (model 3) was run to test partial scalar invariance.
However, the ANN model (Model B1) shows more accurate result especially from 7th to 9th of December but after that all models performed similarly.
Compared to model 1, a nested model (model 2) with h removed resulted in a non-significant degradation in model fit and indicated that this parameter could be omitted.
The second aspect in a modeling workflow is model validation, so that the model results can be unbiasedly assessed.
Table 17 Comparison of model results Model Criterion Symbol CC BAS TV Models as reported Determination R2 adj.
The first column in Tables 4 and 5 presents OLS model results (model 1), the second and third columns of Table 4 present the SAR (model 2a) and SEM (model 3a) model results using a first order contiguity matrix, while the second and third columns of Table 5 present SAR (model 2b) and SEM (model 3b) model results using inverse distances matrix.
Dropping the facility variables from the facility fixed-effects model does not change model results.
If epistemic uncertainty is contained in the energy model, the model results are likely to be more uncertain than the input variables.
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