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? <> <> <>Thanks in advance for any advice! <><> Best regards, Henrik <> -- ************************ Henrik Pärn Department of Biology NTNU 7491 Trondheim Norway +47 735 96282 (office) +47 909 89 255 (mobile) +47 735 96100 (fax) ______________________________________________ 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.
