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
> <>
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