Q: Fitting a model to a (biological) curve - ggplot2 / R I after a bit of help in visualising my data. I'm looking at some experimental data and trying to fit a line to help visualise it more easily; I don't plan on using this for analysis, at least not yet.  
I am using geom_point() to plot my data. The trouble is, the data is time-resolved biological data and often quite noisy so my subgroups overlap and it makes it difficult to visualise. I want therefore to produce an 'average' (please excuse my non-statistical language!) line for each subgroup so I can quickly visualise the trends. 
I have been looking at geom_smooth and the various models that can be used to fit a line.  LOESS takes ages to run (I will typically have ~ 70,000 data points) and doesn't appear to be that accurate.  GAM is better but neither accurately reflect the various parameters I'm looking at. The relevant values are the peak value, the time point for the peak value, and the time of the first inflection point (after the initial lag phase). I have these datapoints available. 
Of note, all the plots begin with (0,0) so the line should ideally pass through that.
As a representative example. n=6
ggplot(ad, aes(Time, y))+geom_point(color='gray', size=1)+
  geom_smooth(method='gam', formula=y ~ s(x, bs = "cs"))+
  geom_smooth(method="loess", colour='red')


I think I need to constrain the data at (0,0) but also somehow use the data (which I have within another dataframe) for the inflection time.
Can anyone suggest any ways to improve my line?  
