On Mon, Dec 13, 2010 at 8:20 AM, Ethan Arenson
<ethan.a.arenson at 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]]
>
>
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