Sensitivity, specificity, and accuracy of the sets were calculated as the proportion of true positive results, true negative results, and true positive plus true negative results, respectively.
These results depend basically on the proportion of true positives, true negatives, false positives, and false negatives chosen from the program to compose the sample.
The accuracy is the proportion of true results (both true positives and true negatives) in the population.
Accuracy is the proportion of true results (both true positive and true negative).
In this study protocol the statistical term accuracy is defined as the proportion of true needle placements (both true positives and true negatives).
This represents the proportion of true positives and true negatives as a proportion of all results.
Sensitivity and specificity were calculated as the proportion of true positives and true negatives (cutoff value >0.001 copies/genome), respectively.
Examining sensitivity and specificity, that is, the proportion of true positives (sensitivity) and true negatives (specificity), which are correctly identified by the four-step decision tree.
Positive predictive values for nonremission and nonimprovement were calculated as the proportion of true positives over the sum of true positives plus false-positives.
Treating PICANet ID as a gold standard, we then calculated the proportion of true matches, false matches, missed matches and true non-matches.
Specifically, in simulations, FastTree 2 recovers a higher proportion of true splits, and on genuine alignments, FastTree 2's topologies tend to have higher likelihoods.
