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The assumption of normality was analysed by visually plotting the estimate against the residuals: no strong associations between residuals and estimates were found.
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Based on the dual concern model, Figure 1 depicts the results obtained in previous studies by visually plotting them strategy by strategy, comparing the three cultures in question.
The fit of the proportional hazards model was checked visually by plotting the incidence rates over time and by entering time-dependent variables into the model.
Similarity between the mean estimated rates was determined visually by plotting the mean and 95% HPDs and by using the data to generate violin plots (violinmplot package in R [ 69, 70]).
Non-linearity of the three MRI variables was checked visually by plotting the martingale residuals 41 and statistically by using a Wald test (using the Stata command nlcheck, with the spline option).
The utility of the final model was assessed visually by plotting the Cox Snell residuals against the Nelson-Aalen cumulative hazard.
Fit of the random effect model was assessed visually by plotting the QQ-plots of the Best Linear Unbiased Predictors (BLUPs) against the normal scores [ 10].
The adequacy of the burnin and subsequent length of the MCMC chain was checked visually by plotting the parameters α and the lnL against the number of generations.
The results can be represented visually by plotting the calculated values and the true values, in the (y,z) plane as in Fig. 3 (a).
Fourth, data was examined visually by plotting the differences between the two measures (difference score = health care provider score minus patient score) against their individual means in Bland-Altman plots.
In our view, the most efficient way of doing so if only few categories are compared, is visually, by plotting the raw data along with the means and credible intervals coming from the model, instead of providing tables with numbers.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com