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This is a generalization of distributions proposed previously, and leads to good goodness-of-fit scores.
The prediction model showed acceptable discrimination (AUC >0.70 but <0.80), a good goodness-of-fit (HL test, P = 0.50) and had a shrinkage factor of 0.83.
Appropriate, albeit not good overall goodness-of-fit indices were found for the initial unidimensional model.
Table 4 shows a summary of the good-of-fit indices for the two alternative models.
Model checking of assumptions and good-of fit tests are conducted for all analyses and further analyses are conducted as required.
The goodness-of-fit plots supported a good data fit of the final model.
All global goodness-of-fit indicators suggest a good fit of the factorial structure of the PTMQ with the data.
The model was found to fit the data well in most cases (P>0.2 for all 'good' fits; χ goodness-of-fit test).
Gd concentration shows a linear relationship up to 1.5 mM with a good of fit r > 0.98 according to Equation2 (Figure 7). Figure 6 Arrangement of nanoparticle tubes for imaging and relaxometry.
This model was reliable and had a good fit (adjusted R2 of fitting = 0.75).
The model had good calibration (goodness of fit P = 0.88).
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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