Question

    ggplot(test,aes(x=timepoints,y= mean,ymax = mean + sde, ymin = mean  - sde)) + 
       geom_errorbar(width=2) +
       geom_point() +
       geom_line() +
       stat_smooth(method='loess') + 
       xlab('Time (min)') +
       ylab('Fold Induction') +          
       opts(title = 'yo')   

I can plot the blue ‘loess’-ed line. But is there a way to find the mathematical function of the blue ‘loess’-ed line?

Answer 1

You can get the predictions for a regular sequence:

 fit <-loess( mean ~ timepoints, data=test)
 fit.points <- predict(fit, newdata=  data.frame(
                  speed = seq(min(timepoints), max(timepoints), length=100)), 
              se = FALSE)
 fitdf <- dataframe(x = seq(min(timepoints), max(timepoints), length=100)
                    y = fit.points)

You can then fit to that set of points with splines of an appropriate degree. Cubic spline fits can be described with greater ease than can loess fits.It would be easier to synchronize an answer to variable names it you had offered a data example to work with. The plot does not seem to be created with that code.