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Whether the statistical model is correctly established or not determines the accuracy of the classification method.
Finally, the likelihood ratio (LR) test was used to verify if the model is correctly specified.
If the outcome model is correctly specified, the weights do not affect the consistency of the estimator (Wooldridge 2007).
In addition, the RESET test (with a p value of 0.3887) indicates that the model is correctly specified with no omitted variables.
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).
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.
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Most of the residues involved in intermolecular interaction are well covered by the electron density, indicating the model was correctly built (Fig. S2).
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%.
The s-GWR model performance was low (R2 = 0.45) and its residuals did not show autocorrelation (Moran's I = −0.001; z-score = 0.003 and p-value = 0.997) indicating that the model was correctly spelled.
All estimated loadings were significant (p < 0.001), the sign obtained was as expected, and the model was correctly identified.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com