You could subtract the mean from the response before fitting, then fit a
model without an intercept.  That would give you four parameters (one for
each level) relative to the mean ...

summary(lm(weight-mean(weight) ~ Diet -1, ChickWeight))

Jean


On Thu, Jun 27, 2013 at 5:47 PM, Shaun Jackman <sjackman@gmail.com> wrote:

> Hi Jean,
>
> contr.treatment(4) shows what the default contrast matrix looks like
> for a factor with 4 levels. What function do I use to create a
> contrast matrix to compare each level with the global mean (four
> comparisons in total), and produce a table similar to `summary.lm`?
>
> Thanks,
> Shaun
>
>
> On 26 June 2013 05:50, Adams, Jean <jvadams@usgs.gov> wrote:
> > Shaun,
> >
> > See the help on contrasts ...
> >      ?contr.treatment
> >
> > Jean
> >
> >
> > On Tue, Jun 25, 2013 at 7:07 PM, Shaun Jackman <sjackman@gmail.com>
> wrote:
> >>
> >> Hi,
> >>
> >> I've used `lm` to create a linear model of a continuous variable
> >> against a factor variable with four levels using an example R data set
> >> (see below). By default, it uses a treatment contrast matrix that
> >> compares each level of the factor variable with the first reference
> >> level (three comparisons in total). I'd like to compare each level
> >> with the global mean (four comparisons in total), and produce a table
> >> similar to `summary.lm`. How do I go about this?
> >>
> >> ```r
> >> model <- lm(weight ~ Diet, ChickWeight)
> >> summary(model)
> >> ```
> >>
> >> Thanks,
> >> Shaun
> >>
> >> ______________________________________________
> >> 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.
> >
> >
>

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