There's a pretty good section in the R book by Crawley on contrast statements in R, including some discussion of the contrasts being orthogonal.
I would say you should just make your own table and sort it out there- if you have equal sample sizes, then the contrast coefficients along the row should sum to 0. Further, if the column sums also sum to zero, then you know you are within your experimentwise error rate as well. Sokal and Rohlf (1995) has a good discussion about this as well, as does Zar 1999 (I think... can't be sure). Alternatively, there's some good stuff using hypothesis() in John Fox's car() package for setting up appropriate contrast statements in linear models. Mike On Fri, Dec 3, 2010 at 7:47 AM, S Ellison <s.elli...@lgc.co.uk> wrote: > > A common point made in discussion of contrasts, type I, II, III SS etc > is that for sensible comparisons one should use contrasts that are > 'orthogonal in the row-basis of the model matrix' (to quote from > http://finzi.psych.upenn.edu/R/Rhelp02/archive/111550.html) > > Question: How would one check, in R, that this is so for a particular > fitted linear model object? > > Steve Ellison > > > > ******************************************************************* > This email and any attachments are confidential. Any u...{{dropped:20}} ______________________________________________ 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.
