Recreate ggplot's geom_smooth CI background - in R basic?

Question

Hi all,

I wish to recreate this graph:

(from here)

Using R base graphics.

I have no clue how to do that. Any advice ?

(My motivation is that I wish to create a plot where the line width (and/or color) will reflect another dimension. Until now - ggplot2 is the only place I found in R for how to do this. I would be happy to be able to do this also in base R)

Answer 1

See help(polygon) and example(polygon) (esp the Brownian motion example) -- the varying width is pretty common in some fields to show variability through time.

The same example is also in demo(graphics):

## An example showing how to fill between curves.

par(bg="white")
n <- 100
x <- c(0,cumsum(rnorm(n)))
y <- c(0,cumsum(rnorm(n)))
xx <- c(0:n, n:0)
yy <- c(x, rev(y))
plot(xx, yy, type="n", xlab="Time", ylab="Distance")
polygon(xx, yy, col="gray")
title("Distance Between Brownian Motions")

Answer 2

And to get the smooth curve, look at loess and predict.loess

Answer 3

I don't know if exactly replicating the graph is possible in base graphics. In grid graphics it is possible. Nevertheless, the following code gets you an example that's something like what you want. Adapt it to the data set.

n <- 20
x <- rnorm(n)
y <- rnorm(n)
o <- order(x)
x <- x[o]
y <- y[o]
m <- loess(y~x, span = 1) #ggplot seems to smooth more than default
f <- predict(m, se = TRUE)
ci <- f$se * qt(0.975, f$df)
cih <- f$fit + ci
cil <- f$fit - ci
plot(x,y, ylim = c(min(cil,y), max(cih,y)))
lines(x, f$fit, lwd = 2)
xx <- c(x, rev(x))
yy <- c(cil, rev(cih))
polygon(xx, yy, col="#A9A9A930", border = NA)

Answer 4

OK, I spent a little too much time messing with this... note the last line is the ggplot version so you can compare the two.

#loess and error curves almost just like ggplot2
op <- par(las=1, mar = c(3,3,1,1))
n <- 30
x <- sort(rnorm(n)) #(varying density in predictor)
x <- x + abs(min(x))
x <- x/max(x)*2*pi 
y <- sin(x)+rnorm(n) #(curvy)
m <- loess(y~x)
xx <- seq(min(x), max(x), (max(x)-min(x))/1000) #increase density of values to predict over to increase quality of curve
f <- predict(m, xx, se = TRUE)
ci <- f$se * qt(0.975, f$df)
cih <- f$fit + ci
cil <- f$fit - ci
plot(x,y, ylim = c(min(cil,y), max(cih,y)), cex.axis = 0.85, xlab = '', ylab = '', type = 'n')
title(xlab = 'x', ylab = 'y',line = 2)
grid(col = 'gray')
points(x,y, pch = 19, cex = 0.65)
lines(xx, f$fit, col = 'blue', lwd = 1.2)
xx <- c(xx, rev(xx))
yy <- c(cil, rev(cih))
polygon(xx, yy, col=rgb(0.1,0.1,0.1,0.25), border = NA)
par(op)

#qplot(x,y, geom = 'point') + stat_smooth()

Answer 5

Would geom_ribbon in GGPlot be what you need? This creates a variable-width line.