AddLm: 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 prediction intervals.

Usage

## S3 method for class 'default':
AddLm(x, y, ...) 
## S3 method for class 'formula':
AddLm(formula, data, ...) 
## S3 method for class 'lm':
AddLm(x, col = "blue", lwd = 2, lty = "solid", 
         type = "l", n = 100, conf.level = 0.95, args.cband = NULL, 
         pred.level = NA, args.pband = NULL, ...)

Arguments

x, y

vectors giving the coordinates of the points in the scatter plot.

formula

a formula specifying the numeric response and one numeric predictor. Will be coerced to a formula if necessary.

data

an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from whi

col

linecolor of the line.

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".

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).

...

further arguments are not used specifically.

Value

nothing

Details

It's sometimes illuminating to plot a regression line with it's prediction, resp. confidence intervals over an existing xy-plot. This only makes sense, if just a simple regression model y ~ x is to be visualized.

Examples

Run this code

par(mfrow=c(1,2))

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

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