Given the following data, I want a scatterplot with the data points and the predictions from the regression.

Sigma <- matrix(c(1,.6,1,.6), 2)
mu <- c(0,0)
dat <- mvrnorm(5000, mu, Sigma)

x <- dat[,1] * 50 + 200
y <- dat[,2] * 50 + 200

fm <- lm(y ~ x)

### This gives the regression line, but not the data
xyplot(y ~ x,
               type = c('g', 'p'),
               panel = function(x, y){
               panel.lines(x, predict(fm))
               }
)

### This gives both data but as point
xyplot(y + predict(fm) ~ x,
               type = c('g', 'p'),
               )

I know I can add an abline easily, but my problem is a bit more complex and the code above is just an example. What is the best way for the predicted data to form a solid line and let the data points remain as points

Harold

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