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There were differences between models with different protection functions.
Major differences in the model explanatory power were observed mainly depending on the scale of the explanatory variable, i.e. either linear or logarithmic, whereas the differences between models with different link functions were comparatively minor (Table 1).
For PM10, more significant differences between the models are observed, especially between models with different spatial resolutions.
The AIC index, where the model with the lowest AIC value is defined as the best solution, can be used for comparisons between models with different numbers of parameters.
Our work suggests that these two approaches can produce different results and that parameter estimates are not easily transferred between models with different survivorship functions.
During EFA, the optimal number of factors was determined by comparing the AIC and BIC between models with different numbers of factors.
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To test the effects of interactions between variables, models with different complexity levels were tested at the species level.
Figure 10 shows that this holds in a wide range of SIR. Fig. 10 Comparison between AR models with different orders with respect to mean squared error as a function of the signal-to-interference ratio.
They are mostly used for comparative purposes such as choosing between different models with different predictors rather than to evaluate, as in our case, the absolute accuracy of a single model over a period of time (8, 17, 18).
An F test is used to demonstrate whether there is a significant difference between the two models with different orders.
Parallel validation qualitatively compared similarity between other published models with different decision problems.
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