Plot the observed and fitted values from a linear regression using xyplot() from the lattice package

Question

I can create simple graphs. I would like to have observed and predicted values (from a linear regression) on the same graph. I am plotting say Yvariable vs Xv

Answer 1

Another option is to use panel.lmlineq from latticeExtra.

library(latticeExtra)
set.seed(0)
xsim <- rnorm(50, mean = 3)
ysim <- (0 + 2 * xsim) * (1 + rnorm(50, sd = 0.3))

## basic use as a panel function
xyplot(ysim ~ xsim, panel = function(x, y, ...) {
  panel.xyplot(x, y, ...)
  panel.lmlineq(x, y, adj = c(1,0), lty = 1,xol.text='red',
                col.line = "blue", digits = 1,r.squared =TRUE)
})

Answer 2

Here is one option for the observed and predicted values in a single plot as points. It is easier to get the regression line on the observed points, which I illustrate second

First some dummy data

set.seed(1)
x <- runif(50)
y <- 2.5 + (3 * x) + rnorm(50, mean = 2.5, sd = 2)
dat <- data.frame(x = x, y = y)

Fit our model

mod <- lm(y ~ x, data = dat)

Combine the model output and observed x into a single object for plott

res <- stack(data.frame(Observed = dat$y, Predicted = fitted(mod)))
res <- cbind(res, x = rep(dat$x, 2))
head(res)

Load lattice and plot

require("lattice")

xyplot(values ~ x, data = res, group = ind, auto.key = TRUE)

The resulting plot should look similar to this

To get just the regression line on the observed data, and the regression model is a simple straight line model as per the one I show then you can circumvent most of this and just plot using

xyplot(y ~ x, data = dat, type = c("p","r"), col.line = "red")

(i.e. you don't even need to fit the model or make new data for plotting)

The resulting plot should look like this

An alternative to the first example which can be used with anything that will give coefficients for the regression line is to write your own panel functions - not as scary as it seems

xyplot(y ~ x, data = dat, col.line = "red",
       panel = function(x, y, ...) {
         panel.xyplot(x, y, ...)
         panel.abline(coef = coef(mod), ...) ## using mod from earlier
       }
      )

That gives a plot from Figure 2 above, but by hand.

Assuming you've done this with caret then

mod <- train(y ~ x, data = dat, method = "lm",
             trControl = trainControl(method = "cv"))

xyplot(y ~ x, data = dat, col.line = "red",
       panel = function(x, y, ...) {
         panel.xyplot(x, y, ...)
         panel.abline(coef = coef(mod$finalModel), ...) ## using mod from caret
       }
      )

Will produce a plot the same as Figure 2 above.