On Mon, Dec 13, 2010 at 8:20 AM, Ethan Arenson <ethan.a.aren...@gmail.com> wrote: > Consider the following missing data problem: > > y = c(1, 2, 2, 2, 3) > a = factor(c(1, 1, 1, 2, 2)) > b = factor(c(1, 2, 3, 1, 2)) > fit = lm(y ~ a + b) > anova(fit) > > Analysis of Variance Table > > Response: y > Df Sum Sq Mean Sq F value Pr(>F) > a 1 0.83333 0.83333 1.3637e+33 < 2.2e-16 *** > b 2 1.16667 0.58333 9.5461e+32 < 2.2e-16 *** > Residuals 1 0.00000 0.00000 > --- > Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > Warning message: > In anova.lm(fit) : > ANOVA F-tests on an essentially perfect fit are unreliable > > I am trying to understand how R computes sums of squares. I know that R > makes a FORTRAN call to dqrls to make a QR decomposition of the design > matrix, which returns (among other things),
> fit$effects > (Intercept) a2 b2 b3 > -4.472136e+00 9.128709e-01 7.715167e-01 7.559289e-01 2.471981e-17 > > Can anyone elaborate on how R computes these effects? I am not satisfied > with the explanation that R provides with the help(effects) command. Q'y > Thanks in advance. > > Ethan > > [[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. > > ______________________________________________ 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.
