Dataset |
X
min
| Rho1 | Rho1 p-value | Rho2 | Rho2 p-value |
---|
mac1
| -1.50 | -0.08 | 1.36E-03 | 0.16 | 0.00E+00 |
yer033c
| -0.70 | -0.10 | 2.30E-08 | 0.11 | 3.70E-09 |
cup5
| -0.93 | -0.17 | 0.00E+00 | 0.11 | 5.44E-10 |
spf1
| -1.44 | -0.18 | 0.00E+00 | 0.20 | 0.00E+00 |
ymr031c
| -0.89 | -0.17 | 0.00E+00 | 0.11 | 3.43E-10 |
vm8
| -0.82 | -0.10 | 7.98E-09 | 0.10 | 1.66E-08 |
yap1
| -0.97 | -0.23 | 0.00E+00 | 0.12 | 1.59E-10 |
sod1
| -1.54 | -0.09 | 3.68E-04 | 0.15 | 0.00E+00 |
fre6
| -1.77 | -0.13 | 2.69E-10 | 0.06 | 2.46E-04 |
cin5
| -0.28 | -0.12 | 2.17E-14 | 0.11 | 1.09E-07 |
- Use of Spearman rank correlation for absolute residuals to detect heteroscedasticity [25] in ten datasets from Hughes et al. [18]. Empirical hyperbolas (here they are based on supsmu smoother) have minima around sample means. As a result, we use two subintervals to compute Spearman rank correlation coefficient: from minus infinity to X
min
(log2(Cy3) axis) and from X
min
to plus infinity. We note that sign of Spearman rank correlation always coincides with the sign of first derivative for empirical hyperbolas at a given subinterval (compare Figure 20). Rho1, Spearman coefficient of rank correlation for the former subinterval; Rho1 p-value, p-values for values in column Rho1; Rho2, Spearman coefficient of rank correlation for the latter subinterval; Rho2 p-value, p-values for values in column Rho2 (p-values are given in scientific notation, 0.00E+00 means that the respective p-value was less than 10-16).