on 01/13/2009 11:25 AM Peter Dalgaard wrote: > 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.
Good points on both accounts Peter. Thanks, Marc ______________________________________________ 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.
