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Based on the model obtained a feedforward as well as a feedback controller were designed.
The corrected model obtained a statistical significant result explaining 13.3% of the variance but with moderate effect.
Confirmatory factor analyses showed that the one-factor model obtained a good fit for the GPQ-18 and acceptable for the GPQ-9.
From the model obtained, a milder reaction condition with a theoretical 98.0% of FAMEs content was created and tested to evaluate its accuracy.
A one factor model obtained a standardized root mean residual RMSR = .12, greater than recommendation (RMSR <.06; Ferrando & Lorenzo-Seva, 2013), and a lower percentage of explained variance (34.65%) than the other models.
Based on this and the resulting models, τ = 10 was selected for this inversion, considering the smoothness of the model obtained, a small change in the RMS, and that the general features of the model are consistent between the inversions with most other τ values (see Fig. 4).
Similar(50)
The discrimination ability model obtained an AUC (95 % CI) of 0.808 (0.72 to 0.89).
The discrimination ability model obtained an AUC (95%% CI) 0.81 (0.72 to 0.898).
The model obtained an R2p of 0.93 and RPD of 4.09, indicating that the model is adequate for analytical purposes.
Also, the two factor model obtained an unacceptable standardized root mean residual model fit (RMSR = .09), and also a lower percentage of explained variance (53.99%).
On the training data, our optimal model obtained an accuracy of 80.5%.
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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