Hi: Try this:
library('lattice') xyplot(y ~ x, type = c('g', 'p'), panel = function(x, y, ...){ panel.xyplot(x, y, ...) panel.lines(x, predict(fm), col = 'black', lwd = 2) } ) HTH, Dennis On Wed, Nov 23, 2011 at 9:18 AM, Doran, Harold <hdo...@air.org> wrote: > 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. > ______________________________________________ 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.