How to plot Fitted values, observed values and confidence interval and prediction intervals in one plot

Question

I've been tasked with plotting the plot explained in the title here's my code so far:

model<-lm(y~.,data=data)
pred <- prediction(model,data=data,interval = 'confidence')
lwr<-pred[76:150]
upr<-pred[151:225]
interval<-cbind(lwr,upr)

I'm not sure where to go after this because I can't figure out how plot the interval.

Answer 1

You can do this with ggplot2

library(tidyverse)

data(mtcars)


mylm <- lm(mpg ~ wt, data = mtcars)
summary(mylm)

myc <- predict(mylm, newdata = mtcars$wt, interval = "confidence")

fit <- myc[,1]
low <- myc[,2]
high <- myc[,3]


myp <- predict(mylm, newdata = mtcars, interval = "predict")



mtcars %>% 
  ggplot() +
  geom_point(aes(x = wt, y = mpg)) +
  geom_point(aes(x = wt, y = fit), color = "green") +
  geom_line(aes(x = wt, y = fit), color = "green") +
  geom_point(aes(x = wt, y = low), color = "red") +
  geom_line(aes(x = wt, y = low), color = "red") +
  geom_point(aes(x = wt, y = high), color = "red") +
  geom_line(aes(x = wt, y = high), color = "red") +
  geom_point(aes(x = wt, y = myp[,1]), color = "blue") +
  geom_line(aes(x = wt, y = myp[,1]), color = "blue") +
  geom_point(aes(x = wt, y = myp[,2]), color = "darksalmon") +
  geom_line(aes(x = wt, y = myp[,2]), color = "darksalmon") +
  geom_point(aes(x = wt, y = myp[,3]), color = "darksalmon") +
  geom_line(aes(x = wt, y = myp[,3]), color = "darksalmon") 

Fitted are the blue, the confidence is the red, and the prediction the dark salmon, observed are the black.

By the way I know you can place wt and mpg into the ggplot() and have it affect all geom's but I just prefer to do it this way.