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Figure 3 | BMC Bioinformatics

Figure 3

From: Detecting outliers when fitting data with nonlinear regression – a new method based on robust nonlinear regression and the false discovery rate

Figure 3

Choosing a value for Q. The value of Q determines how aggressively the method will remove outliers. This figure shows three possible values of Q with small and large numbers of data points. Each graph includes an open symbols positioned just far enough from the curve to be barely defined as an outlier. If the open symbols were moved any closer to the curve, they would no longer be defined to be outliers. If Q is set to a low value, fewer good points will be defined as outliers, but it is harder to identify outliers. The left panel shows Q = 0.1%, which seems too low. If Q is set to a high value, it is easier to identify outliers but more good points will be identified as outliers. The right panel shows Q = 10%. We recommend setting Q to 1% as shown in the middle panels.

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BMC Bioinformatics

ISSN: 1471-2105

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