lines.lm {DescTools} | R Documentation |
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.
## S3 method for class '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, ...)
x |
linear model object as result from lm(y~x). |
col |
linecolor of the line. Default is the color returned by |
lwd |
line width of the line. |
lty |
line type of the line. |
type |
character indicating the type of plotting; actually any of the |
n |
number of points used for plotting the fit. |
conf.level |
confidence level for the confidence interval. Set this to |
args.cband |
list of arguments for the confidence band, such as color or border (see |
pred.level |
confidence level for the prediction interval. Set this to NA, if no prediction band should be plotted.
Default is |
args.pband |
list of arguments for the prediction band, such as color or border (see |
xpred |
a numeric vector |
... |
further arguments are not used specifically. |
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.
nothing
Andri Signorell <andri@signorell.net>
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)