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Adjusting the coefficients to minimize mean errors from estimated densities. .
Adjusting the coefficients to minimize mean errors from estimated densities.
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The mean error from the centre of the target was the KA score.
We found significant (p < 0.05) increases in mean error from baseline to 7 days of treatment in the omeprazole (p = 0.038) and esomeprazole (p = 0.038) groups, with a large effect size.
As described previously (Brauer et al. 2008), we used the coefficient of determination (R ) and estimated mean error from leave-one-out cross validation analysis to evaluate the performance of these models.
These mean error measures from the simulations were compared with analytical computations, which we derived (Appendix S1).
For all of the datasets processed with Agilent Feature Extraction software only, the mean residual errors from using the Imin pooling metric are always less than or equal to the corresponding mean residual errors from using the Iavg pooling metric.
Table 3 > summarizes mean prediction errors from the aforementioned figures.
Relative root mean square errors from the cross validation was 46.8 % for the LMM and 58.1 % for the k-NN.
The trends in the mean residual errors from the unpaired reference sample analysis agree with the results from the direct comparison analyses.
Both relative root mean square errors from model predictions (RMSE %) and LOCOCV (RMSECV %) were calculated and presented for each category of vegetation and land use type.
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