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We show that the corresponding chained equations algorithm, which imputes under a set of normal linear regression models, satisfies the non-informative margins condition of Proposition 1 (and hence draws from the same joint model as joint modelling imputation).
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Among the 18 models, only Theng – Lytton, Paute – 1, Paute – 2 and Monismith – 1 models satisfied the limiting values of the proposed performance indicators.
These models satisfy the constraint ∂i(ρ˜ui)= 0, where ˜ρ is a mean density profile and u the velocity field.
According to the results, all models satisfy the evaluation criteria as good models: R > 0.8, R 2 > 0.8, (R_{text{m}}^{ 2}) > 0.5.
30 All models satisfied the proportional odds assumption.
All the logistic models were checked to comply with the requirements of Hosmer-Lemeshow goodness-of-fit test (all the reported models satisfied the goodness-of-fit criterion).
Clearly, for predicted contacts below PPV = 0.4, many models satisfy the constraints better than the native structures, i.e. we would not expect that a better folding protocol would improve the models.
None of the models satisfied the four key stages in the creation of a quality risk prediction model; development and validation were completed, but impact and implementation were not assessed.
If the error term in the regression model satisfies the four assumptions noted earlier, then the model is considered valid.
This process model satisfies the requirement of IEEE Std.
This model satisfies the conservation laws and the entropy dissipation.
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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