How to predict or extend regression lines in ggplot2?

Question

I have a data frame that contains two time series: the dates and version numbers of Emacs and Firefox releases. Using one ggplot2 command it's easy to make a chart that uses loess (in a way that looks a bit amusing, which I don't mind) to turn the points into lines.

How can I extend the lines into the future? I want to determine where and when Emacs and Firefox version numbers will cross, and if there's a way to show an error range, all the better.

Given that ggplot2 is plotting the lines, it must have a model, but I don't see how to tell it to extend the lines, or to get the model out and do something with it.

> library(ggplot2)
> programs <- read.csv("http://www.miskatonic.org/files/se-program-versions.csv")
> programs$Date <- as.Date(programs$Date, format="%B %d, %Y")
> head(programs)
  Program Version       Date
1   Emacs    24.1 2012-06-10
2   Emacs    23.4 2012-01-29
3   Emacs    23.3 2011-03-10
4   Emacs    23.2 2010-05-08
5   Emacs    23.1 2009-07-29
6   Emacs    22.3 2008-09-05
> head(subset(programs, Program == "Firefox"))
   Program Version       Date
18 Firefox      16 2012-10-09
19 Firefox      15 2012-08-28
20 Firefox      14 2012-06-26
21 Firefox      13 2012-06-15
22 Firefox      12 2012-04-24
23 Firefox      11 2012-03-13
> ggplot(programs, aes(y = Version, x = Date, colour = Program)) + geom_point() + geom_smooth(span = 0.5, fill = NA)

(Note: I had to fudge the early Firefox versions and turn 0.1 onto 0.01, etc., because "dot one" and "dot ten" are equal arithmetically. I know Firefox is releasing every six weeks now, but they don't exist yet, and I'm interested in a general answer to this prediction question.)

Answer 1

As @Glen mentions you have to use a stat_smooth method which supports extrapolations, which loess does not. lm does however. What you need to do is use the fullrange parameter of stat_smooth and expand the x-axis to include the range you want to predict over. I don't have your data, but here's an example using the mtcars dataset:

ggplot(mtcars,aes(x=disp,y=hp)) + geom_point() + xlim(0,700) +
stat_smooth(method="lm",fullrange=TRUE)

Answer 2

You would have to predict the values for future observations outside of ggplot2 and then plot the predicted values, you could also get a confidence interval for these predictions.

Look at the loess function, although I'm not sure if it does predictions outside your data range, I'm sure some smooth function does however.

However it is usually not wise to predict values outside your data range. I would not put much trust in these predictions.

You may want to investigate predicting values using a time series model.