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Calibration of the model was visually checked by plotting the predicted probabilities estimated by the prediction models against the observed proportion of first CHD events (Framingham and UKPDS) and fatal CHD events (SCORE).
The leverage of a single cell line on the prediction model was investigated by plotting the predicted resistance value originating from the leave-one-out-cross-validation versus the measured resistance index (Figure S6).
Calibration of the model predictions, which is related to reliability, was assessed by plotting the predicted individual probability against the observed sick leave.
This is otherwise known as the model calibration and was assessed by plotting the predicted probability of a fracture and the observed frequency of fractures.
An alternative way of visualizing this relation is by plotting the predicted states (or parameter) values as a function of the true values.
The reliability of the model will be determined by plotting the predicted probabilities of poor functional outcome against the observed frequencies in a calibration plot and by calculating the C-statistic (discrimination) [ 37].
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Calibration, that is, the agreement between observed and predicted frequencies of a given outcome, will be determined by plotting the mean predicted versus observed cases of chronic LBP for 10 risk stratification levels.
The fit was also presented graphically, by plotting the monthly predicted and observed values.
For example, if age is shown to be an important predictor of dose, then we will plot the predicted doses by age group.
These data were also visualized by plotting the ratios of predicted to measured dust concentrations sorted by log Koa (SI Figure S8).
Residuals were examined for linearity and normality by inspecting normal probability plots and histograms of the residuals and by plotting the residuals versus the predicted values.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com