Marc Schwartz wrote: > >> DF.fitted > Y A B F.lm > 1 21.86773 0 a 23.52957 > 2 25.91822 0 a 23.52957 > 3 20.82186 0 a 23.52957 > 4 42.97640 1 a 36.18023 > 5 36.64754 1 a 36.18023 > 6 30.89766 1 a 36.18023 > 7 47.43715 0 b 46.50615 > 8 48.69162 0 b 46.50615 > 9 47.87891 0 b 46.50615 > 10 53.47306 1 b 59.15681 > 11 62.55891 1 b 59.15681 > 12 56.94922 1 b 59.15681 > 13 61.89380 0 c 62.98442 > 14 53.92650 0 c 62.98442 > 15 70.62465 0 c 62.98442 > 16 74.77533 1 c 75.63508 > 17 74.91905 1 c 75.63508 > 18 79.71918 1 c 75.63508 > > > # Now get the means of the fitted values across > # the combinations of A and B > M <- with(DF.fitted, tapply(F.lm, list(A = A, B = B), mean)) > >> M > B > A a b c > 0 23.52957 46.50615 62.98442 > 1 36.18023 59.15681 75.63508 > > > Thus: > > # Intercept = *fitted* mean at A = 0; B = "a" >> M["0", "a"] > [1] 23.52957
Actually, notice that you are averaging identical values, so the "mean" in the tapply is slightly misleading. Notice also that the intercept may be defined even when _no_ observations have zero entries in the design matrix. This is the usual case in linear regression, for instance, but it can happen in factorial designs (unbalanced, or using other than treatment contrasts) as well. -- O__ ---- Peter Dalgaard Øster Farimagsgade 5, Entr.B c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~~~~~~~~~ - (p.dalga...@biostat.ku.dk) FAX: (+45) 35327907 ______________________________________________ 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.
