Loess or lowess stands for “locally weighted smoothing”. It can be applied through the use of a generalised additive model (GAM). The technique can accommodate nonlinear and non-monotonic functions, thus offering a flexible nonparametric modelling tool. In loess, each observed value is replaced by a predicted value, generated by connecting the central point from a weighted regression for a given span (neighbourhood) of the data (Hastie and Tibshirani 1990). The polynomial is fitted using weighted least squares, giving more weight to points near the point whose response is being estimated and less weight to points further away.