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I have two data frames raw and coef : one containing raw data the other containing modelling coefficients that I have derived from the raw data. The first data frame raw contains : Time (0 to 900 seconds) OD for many Variants and four runs. The second data frame coef contains : one row per Variant/run combination, with the individual coefficients ( M , D.1 and t0.1 ) in that row. I have plotted the raw data split per Variant and colored by runID , without a problem. But, now I want to overlay the model curves according to the runID . Since the modelling coefficients are in a different data frames, with different dimensions, I can't just cbind them. stat_function won't work for me. I can get only one curve showing at a time. I have tried with a for loop , adding a stat_function layer each time: p <- ggplot(temp, aes(Time, OD)) + geom_point(aes(colour = runID), size = 2) #works fine! calc <- function(x){temp.n$M[ID] * (1 - exp(temp.n$D.1[ID] * temp.n$t0.1[ID] - x)))} for(ID in 1:length(unique(temp.n$runID))) { p <- p + stat_function(fun = calc) } print(p) At the end, all p returns is the plot of the raw data, and the final curve from the looping bit. p seems to revert to its original state every time I try to add a new stat_function layer. Any ideas ?

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