lines.lm: Add a Linear Regression Line

Description

Add a linear regression line to an existing plot. The function first calculates the prediction of a lm object for a reasonable amount of points, then adds the line to the plot and inserts a polygon with the confidence and, if required, the prediction intervals. In addition to abline the function will also display polynomial models.

Usage

# S3 method for lm
lines(x, col = Pal()[1], lwd = 2, lty = "solid",
      type = "l", n = 100, conf.level = 0.95, args.cband = NULL,
      pred.level = NA, args.pband = NULL, xpred = NULL, ...)

Value

nothing

Arguments

x: linear model object as result from lm(y~x).
col: linecolor of the line. Default is the color returned by Pal()[1].
lwd: line width of the line.
lty: line type of the line.
type: character indicating the type of plotting; actually any of the types as in plot.default. 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.cband: list of arguments for the confidence band, such as color or border (see DrawBand).
pred.level: confidence level for the prediction interval. Set this to NA, if no prediction band should be plotted. Default is 0.95.
args.pband: list of arguments for the prediction band, such as color or border (see DrawBand).
xpred: a numeric vector c(from, to), if the x limits can't be defined based on available data, xpred can be used to provide the range where the line and especially the confidence intervals should be plotted.
...: further arguments are not used specifically.

Author

Andri Signorell <andri@signorell.net>

Details

It's sometimes illuminating to plot a regression line with its prediction, resp. confidence intervals over an existing scatterplot. This only makes sense, if just a simple linear model explaining a target variable by (a function of) one single predictor is to be visualized.

Examples

Run this code

opar <- par(mfrow=c(1,2))

plot(hp ~ wt, mtcars)
lines(lm(hp ~ wt, mtcars), col="steelblue")

# add the prediction intervals in different color
plot(hp ~ wt, mtcars)
r.lm <- lm(hp ~ wt, mtcars)
lines(r.lm, col="red", pred.level=0.95, args.pband=list(col=SetAlpha("grey",0.3)) )

# works with transformations too
plot(dist ~ sqrt(speed), cars)
lines(lm(dist ~ sqrt(speed), cars), col=hred)

plot(dist ~ log(speed), cars)
lines(lm(dist ~ log(speed), cars), col=hred)

# and with more specific variables based on only one predictor
plot(dist ~ speed, cars)
lines(lm(dist ~ poly(speed, degree=2), cars), col=hred)

par(opar)

Run the code above in your browser using DataLab