Add a Loess or a Spline Smoother

Add a loess smoother to an existing plot. The function first calculates the prediction of a loess object for a reasonable amount of points, then adds the line to the plot and inserts a polygon with the confidence intervals. 

# S3 method for class 'loess'
lines(x, col = Pal()[1], lwd = 2, lty = "solid",
      type = "l", n = 100, conf.level = 0.95, args.band = NULL, ...)

# S3 method for class 'smooth.spline'
lines(x, col = Pal()[1], lwd = 2, lty = "solid",
      type = "l", conf.level = 0.95, args.band = NULL, ...)

# S3 method for class 'SmoothSpline'
lines(x, col = Pal()[1], lwd = 2, lty = "solid",
      type = "l", conf.level = 0.95, args.band = NULL, ...)

Arguments

x

the loess or smooth.spline object to be plotted.

col

linecolor of the smoother. Default is DescTools's col1.

lwd

line width of the smoother.

lty

line type of the smoother.

type

type of plot, defaults to "l".

n

number of points used for plotting the fit.

conf.level

confidence level for the confidence interval. Set this to NA, if no confidence band should be plotted. Default is 0.95.

args.band

list of arguments for the confidence band, such as color or border (see DrawBand).

...

further arguments are passed to the smoother (loess() or SmoothSpline()).

Note

Loess can result in heavy computational load if there are many points!

Author

Andri Signorell <andri@signorell.net>

Examples

par(mfrow=c(1,2))

x <- runif(100)
y <- rnorm(100)
plot(x, y)
lines(loess(y~x))

plot(temperature ~ delivery_min, data=d.pizza)
lines(loess(temperature ~ delivery_min, data=d.pizza))


plot(temperature ~ delivery_min, data=d.pizza)
lines(loess(temperature ~ delivery_min, data=d.pizza), conf.level = 0.99,
            args.band = list(col=SetAlpha("red", 0.4), border="black") )

# the default values from scatter.smooth
lines(loess(temperature ~ delivery_min, data=d.pizza,
            span=2/3, degree=1, family="symmetric"), col="red")