Combining weighting with robust nonlinear regression or outlier identification

Robust nonlinear regression

Robust regression is a form of weighting. Points far from the curve get less weight. Therefore, as we explain in reference 1, it doesn't make sense to perform robust regression using unequal weights. The problem is that outliers can get too much weight.

If you choose both unequal weighting and robust fitting, therefore, Prism does the fitting assuming equal weights. However it uses your weighting choice when creating a table of residuals, and when counting the number of outliers (a choice you can make in the preferences tab).

Outlier identification

If you choose both unequal weighting and automatic outlier removal, Prism first fits using robust regression (ignoring your weighting choice), and then uses the weighting factors in identifying the outliers, as explained in reference 1.

1. Motulsky HM and Brown RE, Detecting outliers when fitting data with nonlinear regression – a new method based on robust nonlinear regression and the false discovery rate, BMC Bioinformatics 2006, 7:123.