How to plot a comparisson of two fixed categorical values for linear regression of another continuous variable

Question

So I want to plot this:

lmfit = lm (y ~ a + b)

but, \"b\" only has the values of zero and one. So, I want to plot two separate regre

Answer 1

Given you have an additive lm model to begin with, drawing the lines is pretty straightforward, even though not completely intuitive. I tested it with the following simulated data:

y <- rnorm(30)
a <- rep(1:10,times=3)
b <- rep(c(1,0),each=15)

LM <- lm(y~a+b)

You have to access the coefficient values in the lm. Its is:

LM$coefficients

Here comes the tricky part, you have to assign the coefficients for each line.

The first one is easy:

abline(LM$coef[1],LM$coef[2])

The other one is a bit more complicated, given R works with additive coefficients, so for the second line you have:

abline(LM$coef[1]+LM$coef[3],LM$coef[2])

I hope this is what you was expecting

Answer 2

You might want to consider using predict(...) with b=0 and b=1, as follows. Since you didn't provide any data, I'm using the built-in mtcars dataset.

lmfit   <- lm(mpg~wt+cyl,mtcars)
plot(mpg~wt,mtcars,col=mtcars$cyl,pch=20)
curve(predict(lmfit,newdata=data.frame(wt=x,cyl=4)),col=4,add=T)
curve(predict(lmfit,newdata=data.frame(wt=x,cyl=6)),col=6,add=T)
curve(predict(lmfit,newdata=data.frame(wt=x,cyl=8)),col=8,add=T)

Answer 3

Unless I've misunderstood the question, all you have to do is run abline again but on a model without the b term.

abline(lm(y~a),col="red",lwd=2)