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This model is plotted in Fig. 15.
The best fit of this model is plotted together with the data set.
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To evaluate the robust regression model, the amount of ET estimated by this model was plotted versus the calculated values.
To evaluate the MFP model, the amount of ET estimated by this model was plotted versus the calculated values.
To evaluate the Bayesian regression model, the amount of ET estimated by this model was plotted versus the calculated values.
The fitted and predicted values of this model were plotted in Figure 2, and the associated environmental variables are shown in Figure 3. Figure 4 showed the time series profile for positive influenza counts in Maricopa County.
The ROC curve for this model was plotted in Fig. 1a.
Deviance residuals for this model were plotted against the linear predictor and were randomly scattered centred around a residual value of zero ranging between −3.86 and 3.33, which suggests the data have not been mis-modelled.
This model was plotted with the middle 80% of the distribution of DBP for each SBP group, and the HR of a DBP of 90 mmHg in the SBP <160 mmHg category was assigned a reference value of 1.0.
One random realization of this movement model is plotted in Figure 2 and is considered for the ensuing simulations starting with the single-target case.
An ROC for the model was plotted (not shown).
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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