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 [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.