Plotting a 95% confidence interval for a lm object

Question

How can I calculate and plot a confidence interval for my regression in r? So far I have two numerical vectors of equal length (x,y) and a regression object(lm.out). I have made a scatterplot of y given x and added the regression line to this plot. I am looking for a way to add a 95% prediction confidence band for lm.out to the plot. I've tried using the predict function, but I don't even know where to start with that :/. Here is my code at the moment:

x=c(1,2,3,4,5,6,7,8,9,0)
y=c(13,28,43,35,96,84,101,110,108,13)

lm.out <- lm(y ~ x)

plot(x,y)

regression.data = summary(lm.out) #save regression summary as variable
names(regression.data) #get names so we can index this data
a= regression.data$coefficients["(Intercept)","Estimate"] #grab values
b= regression.data$coefficients["x","Estimate"]
abline(a,b) #add the regression line

Thank you!

Edit: I've taken a look at the proposed duplicate and can't quite get to the bottom of it.

Answer 1

You have yo use predict for a new vector of data, here newx.

x=c(1,2,3,4,5,6,7,8,9,0)

y=c(13,28,43,35,96,84,101,110,108,13)

lm.out <- lm(y ~ x)
newx = seq(min(x),max(x),by = 0.05)
conf_interval <- predict(lm.out, newdata=data.frame(x=newx), interval="confidence",
                         level = 0.95)
plot(x, y, xlab="x", ylab="y", main="Regression")
abline(lm.out, col="lightblue")
lines(newx, conf_interval[,2], col="blue", lty=2)
lines(newx, conf_interval[,3], col="blue", lty=2)

EDIT

as it is mention in the coments by Ben this can be done with matlines as follow:

plot(x, y, xlab="x", ylab="y", main="Regression")
abline(lm.out, col="lightblue")
matlines(newx, conf_interval[,2:3], col = "blue", lty=2)