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As shown in Figure 4, the gene frequency distributions for model B fit the data better than those for model A (compare with Figure 3).
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Test 1 for Model A compared this model with the site-specific model M1 (neutral); test 2 compared model A against null model A. Model B was compared with the site-specific model M3 (discrete, K = 2).
Including this additional predictor, we obtained another model with a slightly lower score of information criteria (Akaike's information criteria, 1,539.6 (model A) compared with 1,544.0 (model B); and Schwarz's information criteria, 1,578.7 (model A) compared with 1,580.5 (model B).
In this way it can interrogate whether the better fit of model A compared to model B is statistically significant.
For Grade 3, model C was highly different to model A compared with model B, and Models D and E also produced unstable results; thus, model B was chosen.
We also conducted "branch-sites" CODEML analysis [58] comparing Model A with the corresponding neutral model.
Uplift from model a. Compare the performance of predictive model against random results with lift charts and decline tables.
Model A is compared with M1a (Nearly Neutral) and model B is compared with M3 (discrete).
Missing values in model A were compared with those in model B at T1 and T2.
The branch-site model A was compared with the nearly neutral model (M1).
Phase 2 defined inter- and intraobserver variation, to construct a model to compare with an overall assessment of endoscopic severity.
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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