Henrik Parn wrote: > Dear all, > > I run a linear model with three significant explanatory variabels > x1: a factor with 4 levels > x2 and x3: factors with two levels each > x4: continuous > > model <- lm(y ~ x1 + x2 * x3 + x4) > <> > The data is not perfectly balanced between the different > factor-combinations and I use treatment contrasts. > <> > With an aov.object, I assume I could have used model.tables(aov.object, > type = "means", se = TRUE), to get the means and se for all factor > combinations. > > <>In an lm.object like mine, I calculate the means 'manually' from the > Estimates (for sure it could be done with a script, but fair enough). > <>For the standard error of the means, I started out using formulas of a > variance of a sum of two variables, but I messed things up with the > interaction. Is there a way to calculate the standard error of the means > from Estimates and Std.Error (or other information) from the lm.object?
You might have a look at the effects package by John Fox. http://socserv.mcmaster.ca/jfox/Misc/effects/index.html > <> > <> > <>Thanks in advance for any advice! > <><> > Best regards, > > Henrik > <> -- Chuck Cleland, Ph.D. NDRI, Inc. 71 West 23rd Street, 8th floor New York, NY 10010 tel: (212) 845-4495 (Tu, Th) tel: (732) 512-0171 (M, W, F) fax: (917) 438-0894 ______________________________________________ R-help@stat.math.ethz.ch 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.