Data residuals, however, do not provide all the information needed to check the validity of the DIFI-derived model.
Our analysis is parametric and assumes normality of residuals; however, model residuals for ΔAGB were left-skewed, model residuals for ΔAGBGI and ΔAGBM were right-skewed and residuals of relative ΔAGB and its components were leptokuric.
Small-scale deformation signals are commonly observed in the model's residuals; however, we had difficulties in calculating the best-fit model for all interferograms.
The Residual versus Predicated Plot presented with only a slight fanning of the residuals; however, apart from this there were no systematic trends.
No items had significant chi-square values (after Bonferroni adjustment) or extreme positive fit residuals, however, two items (items 11 and 12) had high negative fit residuals (-2.47 and -2.49 respectively) indicating that these items overfit the model.
The smoothing parameter for time was chosen by minimizing the absolute value of the sum of partial autocorrelation (PAC) of the residuals; however, a minimum of 1 degree of freedom per year was required.
We carried out a sensitivity analysis of the effect of different transformations, e.g. inverse normalizing the trait prior to calculating the residuals; however, this did not materially affect our results.
Cyclic relaxation of compressive residual stress, however, reduces the benefit.
Also, two MRIs were scored as probably residual disease; however, after the consensus meeting, these patients were downgraded to uncertain, therefore diminishing false positive results.
The improved spatial quality mainly benefited from the accurate extraction of residual signals, however, had nothing to do with the quantization method of spatial parameters.
