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 <sjack...@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 <jvad...@usgs.gov> wrote: > > Shaun, > > > > See the help on contrasts ... > > ?contr.treatment > > > > Jean > > > > > > On Tue, Jun 25, 2013 at 7:07 PM, Shaun Jackman <sjack...@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. > > > > > [[alternative HTML version deleted]] ______________________________________________ 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.
