Question

I am trying to show that there is a wierd “bump” in some data I am analysing (it is to do with market share. My code is here:-

qplot(Share, Rate, data = Dataset3, geom=c("point", "smooth"))

(I appreciate that this is not very useful code without the dataset).

Is there anyway that I can get the numeric vector used to generate the smoothed line out of R? I just need that layer to try to fit a model to the smoothed data.

Any help gratefully received.

Answer 1

Yes, there is. ggplot uses the function loess as the default smoother in geom_smooth. this means you can use loess directly to estimate your smoothing parameters.

Here is an example, adapted from ?loess :

qplot(speed, dist, data=cars, geom="smooth")

Use loess to estimate the smoothed data, and predict for the estimated values::

cars.lo <- loess(dist ~ speed, cars)
pc <- predict(cars.lo, data.frame(speed = seq(4, 25, 1)), se = TRUE)

The estimates are now in pc$fit and the standard error in pc$fit.se. The following bit of code extraxts the fitted values into a data.frame and then plots it using ggplot :

pc_df <- data.frame(
    x=4:25,
    fit=pc$fit)

ggplot(pc_df, aes(x=x, y=fit)) + geom_line()