> Specifically my model has one response and two predictors, i.e. it's of the > form > > Y = b_0+b_1*X_1+b_2*X_2 > > Plotting the regression line for a single predictor model > > Y = b_0+b_1*X_1 > > is simple enough, just call abline() with the coefficients returned by lm().
Single variable linear model has only 1 regression line. For two predictors, your regression line! is a surface. (it is not a line anymore) For 3 predictors, your regression line! is a volume etc… > > However, I don't know if this can be adapted to multivariable linear models. Yes, but in a limited manner. Assume your model is Y ~ x1 + x2 + x3 set x2 and x3 constant (for instance, to median of the series) predict (predict.lm) Y.predicted values against x1. Order x1 and Y.predicted values and plot them by lines command on Y ~ x1 scatter plot. Do same thing for other variables. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
