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External validation series were used to validate the model; the 91.8% containing 85 compounds, not considered to fit the model, were correctly classified.
92% of the total cases from the LRPU model were correctly classified, an improvement on the percentage obtained using the PLR model (90%) and significantly higher than the results obtained with the LLR model, 80%.
Many pathophysiological aspects of the disease model were correctly recognized by the GO terms phototransduction, ion transport, apoptosis regulation and cone development.
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Finally, the likelihood ratio (LR) test was used to verify if the model is correctly specified.
Whether the statistical model is correctly established or not determines the accuracy of the classification method.
If the outcome model is correctly specified, the weights do not affect the consistency of the estimator (Wooldridge 2007).
A series of statistical tests suggests that our instruments are valid and that the model is correctly identified: the model passes the tests for under-, weak- and overidentification.
In addition, the RESET test (with a p value of 0.3887) indicates that the model is correctly specified with no omitted variables.
Most of the residues involved in intermolecular interaction are well covered by the electron density, indicating the model was correctly built (Fig. S2).
The ASC is not significant, indicating that respondents have made their choice only on the basis of the attributes in the list (so the model is correctly specified).
The results show that peak frequency to achieve the maximum electrothermal flow, underestimated by the classical model is correctly predicted by the present fully coupled model.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com