Looks like the contrast matrix for indicator is contr.SAS(n), for deviation is contr.sum(n) and for simple is:
(diag(n) - 1/n)[, -1] That works at least for the n = 3 example in the link. Perhaps the others could be checked against SPSS for a variety of values of n to be sure. On Sun, Oct 12, 2008 at 12:32 PM, Chuck Cleland <[EMAIL PROTECTED]> wrote: > On 10/11/2008 3:31 PM, Ted Harding wrote: >> Hi Folks, >> >> I'm comparing some output from R with output from SPSS. >> The coefficients of the independent variables (which are >> all factors, each at 2 levels) are identical. >> >> However, R's Intercept (using default contr.treatment) >> differs from SPSS's 'constant'. It seems that the contrasts >> were set in SPSS using >> >> /CONTRAST (varname)=Simple(1) >> >> I can get R's Intercept to match SPSS's 'constant' if I use >> contr.sum in R. >> >> Can someone please confirm that that is a correct match for >> the SPSS "Simple(1)", with identical effect? >> >> And is there a convenient on-line reference where I can look >> up what SPSS's "/CONTRAST" statements exactly mean? >> I've done a lot of googling, withbout coming up with anything >> satisfactory. >> >> With thanks, >> Ted. > > Hi Ted: > Here are two links with the same content giving a brief description of > SPSS simple contrasts: > > http://www.ats.ucla.edu/stat/spss/library/contrast.htm > http://support.spss.com/productsext/spss/documentation/statistics/articles/contrast.htm > > These pages explain how simple contrasts differ from indicator > (contr.treatment) and deviation (contr.sum) contrasts. For a factor > with 3 levels, I believe you can reproduce SPSS simple contrasts (with > the first category as reference) like this: > >> C(warpbreaks$tension, contr=matrix(c(-1/3,2/3,-1/3,-1/3,-1/3,2/3), > ncol=2)) > ... > attr(,"contrasts") > [,1] [,2] > L -0.3333333 -0.3333333 > M 0.6666667 -0.3333333 > H -0.3333333 0.6666667 > Levels: L M H > > For a factor with 2 levels, like this: > >> C(warpbreaks$wool, contr=matrix(c(-1/2,1/2), ncol=1)) > ... > attr(,"contrasts") > [,1] > A -0.5 > B 0.5 > Levels: A B > > Your description of the effect of SPSS simple contrasts - intercept > coefficient of contr.sum and non-intercept coefficients of > contr.treatment - sounds accurate to me. > > hope this helps, > > Chuck > >> -------------------------------------------------------------------- >> E-Mail: (Ted Harding) <[EMAIL PROTECTED]> >> Fax-to-email: +44 (0)870 094 0861 >> Date: 11-Oct-08 Time: 20:31:53 >> ------------------------------ XFMail ------------------------------ >> >> ______________________________________________ >> 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. > > -- > Chuck Cleland, Ph.D. > NDRI, Inc. (www.ndri.org) > 71 West 23rd Street, 8th floor > New York, NY 10010 > tel: (212) 845-4495 (Tu, Th) > tel: (732) 512-0171 (M, W, F) > fax: (917) 438-0894 > > ______________________________________________ > 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. > ______________________________________________ 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.