They are coding the variables as factors and using orthogonal polynomial contrasts. This:
data.catapult <- data.frame(data.catapult$Distance, do.call(data.frame, lapply(data.catapult[-1], factor, ordered=T))) contrasts(data.catapult$h) <- contrasts(data.catapult$s) <- contrasts(data.catapult$l) <- contrasts(data.catapult$e) <- contr.poly(3, contrasts=F) contrasts(data.catapult$b) <- contr.poly(2, contrasts=F) lm1 <- lm(Distance ~ .^2, data=data.catapult) summary(lm1) gets you closer (same intercept at least), but I can't explain the remaining differences. I'm not even sure why the results to look like they do (interaction terms like "a*b" not "a:b" and one level for each interaction). Hope that helps, Simon Knapp ______________________________________________ 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.
